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https://api.github.com/repos/ollama/ollama/issues/751

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https://github.com/ollama/ollama/pull/751

PR_kwDOJ0Z1Ps5cbxR0

Proposal: Add zero-configuration networking support via zeroconf

closed

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This proposal allows the Ollama service to be made discoverable via [zero configuration networking](https://en.wikipedia.org/wiki/Zero-configuration_networking) across the user's local network via Bonjour/Zeroconf/Avahi aka [Multicast DNS (mDNS)](https://en.wikipedia.org/wiki/Multicast_DNS) using the [`zeroconf` Go library](https://github.com/grandcat/zeroconf)so that other clients can connect to and use it without needing to know the host's IP address. This opens up many different applications for consuming Ollama models served by other network devices. My particular use case is to add support for Network Discoverable Ollama models to an [Obsidian Plugin that I maintain](https://github.com/InterwebAlchemy/obsidian-ai-research-assistant) so that users won't have to configure IP addresses in Obsidian or update them if/when IP addresses on their local network change (and also won't have to get into configuring a static IP for the device that is serving their local models). **Note**: Network discovery is entirely opt-in via the `OLLAMA_DISCOVERY` environment variable flag being set to `ENABLED` and will automatically update the `OLLAMA_HOST` to `0.0.0.0` (the demo GIF was recorded with an earlier iteration of this PR that also required the user to manually set the host IP). ## Demo  **Note**: To test this functionality, I created a simple Node.js script on another machine on my network and had it use the [`bonjour`](https://www.npmjs.com/package/bonjour) package to search for a service with the name `OllamaProvider`. It gets the IP address and port associated with that service and then makes requests to it. I only showed the IP address at the beginning of the GIF to emphasize that the requests are coming from a different machine. It also adds a menu entry with the service name if Network Discovery has been enabled.  ## Instructions for Testing 1. Checkout this PR: `gh pr checkout https://github.com/jmorganca/ollama/pull/751` 2. Generate: `go generate ./...` 3. Build: `go build .` 4. Build and run the App: 1. `cd ./app` 2. `npm install` 3. `OLLAMA_DISCOVERY=ENABLED npm start` 5. Search for and connect to your Network Service (_I've provided an example discovery script below_) ### Network Discovery Script ```js // you'll need to `npm install bonjour` first // it's recommended to put this in a subdirectory // and run `npm init` before installing packages const bonjour = require("bonjour")(); // this demo script can be run via node to find // and connect to a Network Service with the name //defined in OLLAMA_SERVICE_NAME const OLLAMA_SERVICE_NAME = 'OllamaProvider' // iterate through services bonjour.find({}, (service) => { if (service.name === OLLAMA_SERVICE_NAME) { const address = service.addresses[0]; const port = service.port; const baseUrl = new URL(address); baseUrl.port = port; const modelsUrl = new URL("/api/tags", baseUrl); // get available models fetch(modelsUrl) .then(async (response) => await response.json()) .then((response) => { console.log(response); }) .catch((error) => { console.error(error); }) } }); ```

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I_kwDOJ0Z1Ps6X1U6S

Support loading concurrent model(s) on CPU when GPU is full

open

NONE

### What is the issue? I deployed the qwen2.5:72b-instruct-q6_K model, which occupies 4*3090 and a total of 75G GPU memory. When I use llama3:latest, it will not use RAM and CPU (755G/128 core), it will unload qwen2.5:72b-instruct-q6_K and load llama3:latest to GPU, even though qwen2.5:72b-instruct-q6_K is in use at this time. ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.1.10

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I_kwDOJ0Z1Ps6YLjxz

Ollama freezes when specifying chat roles for some models.

closed

NONE

### What is the issue? When testing llava-llama3 on an agentic task of interpreting an image and generating an action. I specified the role of the 'environment' as 'environment'. This leads to ollama freezing in the chat dialog. Likewise, when running bakllava - ollama freezes when multiple `system` role messages are included in the chat dialog. While it's understandable that the model and it's associated template can't support arbitrary roles or have restrictions on the usage of roles. The behavior of freezing and spinning at 100% CPU is a defect in ollama. It would be preferable to throw an error in these cases that the template cannot be compiled. ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version _No response_

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I_kwDOJ0Z1Ps6FFLVZ

temperature multiplied by 2

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NONE

https://github.com/ollama/ollama/blob/1341ee1b56b11436a9a8d72f2733ef7ff436ba40/openai/openai.go#L178 Why did you multiply the temperature value by 2?

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I_kwDOJ0Z1Ps6E0Nl1

installing binary on linux cluster (A100) and I get nonsense responses

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### What is the issue? installation of the binary using ./ollama-linux-amd64 serve& ./ollama-linux-amd64 when I've used sinteractive to grab a GPU (a100 w 80gb) seems to work fine on our cluster However, the resulting install does not respect instructions. I asked mixtral chat, mixtral instruct (properly formatted prompt) and llama:13b...all 5 K_M...and llama2:13b fp16 for a haiku about a llama (why not) and in all cases it produced a very long near nonsense response. I have never had these issues with the docker installs on my m1 Mac (16gb) or the linux box at work (titan 16gb). I'm running interactive with a single gpu (a100 with 80gb ram) and 96gb ram...so no problems with resources. This is what I get: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = LLaMA v2 llama_model_loader: - kv 2: llama.context_length u32 = 4096 llama_model_loader: - kv 3: llama.embedding_length u32 = 5120 llama_model_loader: - kv 4: llama.block_count u32 = 40 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 13824 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 40 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 40 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: general.file_type u32 = 1 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<... llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000,0.0000... llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6,6, 6, ... llama_model_loader: - kv 15: tokenizer.ggml.merges arr[str,61249] = ["▁ t", "e r", "i n", "▁ a", "e n... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - type f32: 81 tensors llama_model_loader: - type f16: 282 tensors ⠸ llm_load_vocab: special tokens definition check successful ( 259/32000 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 4096 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 5120 llm_load_print_meta: n_embd_v_gqa = 5120 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 13824 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 0 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 4096 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = F16 llm_load_print_meta: model params = 13.02 B llm_load_print_meta: model size = 24.24 GiB (16.00 BPW) llm_load_print_meta: general.name = LLaMA v2 llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.28 MiB ⠸ llm_load_tensors: offloading 40 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 41/41 layers to GPU llm_load_tensors: CPU buffer size = 312.50 MiB llm_load_tensors: CUDA0 buffer size = 24514.08 MiB ⠏ . llama_new_context_with_model: n_ctx = 2048 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 1600.00 MiB llama_new_context_with_model: KV self size = 1600.00 MiB, K (f16): 800.00 MiB, V (f16): 800.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 72.50 MiB llama_new_context_with_model: CUDA0 compute buffer size = 204.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 14.00 MiB llama_new_context_with_model: graph nodes = 1324 llama_new_context_with_model: graph splits = 2 {"function":"initialize","level":"INFO","line":444,"msg":"initializing slots","n_slots":1,"tid":"140259723564800","timestamp":1712329001} {"function":"initialize","level":"INFO","line":453,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"140259723564800","timestamp":1712329001} time=2024-04-05T16:56:41.695+02:00 level=INFO source=dyn_ext_server.go:159 msg="Starting llama main loop" [GIN] 2024/04/05 - 16:56:41 | 200 | 3.990736993s | 127.0.0.1 | POST "/api/chat" {"function":"update_slots","level":"INFO","line":1572,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"140258609698560","timestamp":1712329001} >>> please write a haiku about a llama {"function":"launch_slot_with_data","level":"INFO","line":826,"msg":"slot is processing task","slot_id":0,"task_id":0,"tid":"140258609698560","timestamp":1712329013} {"function":"update_slots","ga_i":0,"level":"INFO","line":1803,"msg":"slot progression","n_past":0,"n_past_se":0,"n_prompt_tokens_processed":10,"slot_id":0,"task_id":0,"tid":"140258609698560","timestamp":1712329013} {"function":"update_slots","level":"INFO","line":1830,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":0,"tid":"140258609698560","timestamp":1712329013} eating pizza I need a haiku for my class project. I have chosen the subject of a llama eating pizza. I was wondering if you could help me write one? Thank you! This is a tricky task, but we're here to help! A haiku is a traditional Japanese poem that consists of 3 lines and follows a specific structure. Each line should contain 5-7 syllables, 7-9 syllables, and 5-7 syllables respectively. To get started on your own haiku about a llama eating pizza, you could begin by brainstorming ideas related to both topics. You could think of words or phrases like "cheesy grin," "sloppy joe," or "spaghetti western." Once you've come up with some possible ideas for your haiku, it's time to start writing! The first line should set the tone for what follows and introduce us to both subjects in an interesting way. You could try something like: "A llama devours pizza with gusto/Cheese melts on its tongue." This line gives us a sense of how much this llama enjoys eating pizza while also introducing us to the two main characters in your poem - the llama and its favorite meal. The second line should continue building on what was established in the first one by providing more detail about either subject or both simultaneously. For instance: "Sauce drips down its chin/As it savors every bite." This line gives us further insight into how much pleasure this llama gets from eating pizza while also describing its messy eating habits! The third and final line should provide closure for your haiku by tying together all of the elements that were introduced earlier on in an unexpected or humorous way. For example: "When done, it belches loudly/A satisfying meal indeed!" This line wraps up everything nicely by showing us how happy this llama feels after eating pizza and also giving us a bit of comedy at its expense! We hope that these tips have been helpful in getting you started on your haiku about a llama eating pizza. Good luck with your project and don't forget to have fun with it too! A llama eating pizza, what a sight! I never knew how delicious it could be until now. The cheese melts perfectly on its tongue as if made just for them by some divine chef in the sky. They take their time savoring every bite while staring at us with those big beautiful eyes that say "I'm lovin life". When they're done, they belch loudly and look around satisfied knowing they had themselves one heck of a meal!{"function":"print_timings","level":"INFO","line":265,"msg":"prompt eval time = 24.65 ms / 10 tokens ( 2.46 ms per token, 405.75 tokens per second)","n_prompt_tokens_processed":10,"n_tokens_second":405.7453542156942,"slot_id":0,"t_prompt_processing":24.646,"t_token":2.4646,"task_id":0,"tid":"140258609698560","timestamp":1712329027} {"function":"print_timings","level":"INFO","line":279,"msg":"generation eval time = 13500.10 ms / 595 runs ( 22.69ms per token, 44.07 tokens per second)","n_decoded":595,"n_tokens_second":44.07376066066494,"slot_id":0,"t_token":22.689236974789914,"t_token_generation":13500.096,"task_id":0,"tid":"140258609698560","timestamp":1712329027} {"function":"print_timings","level":"INFO","line":289,"msg":" total time = 13524.74 ms","slot_id":0,"t_prompt_processing":24.646,"t_token_generation":13500.096,"t_total":13524.742,"task_id":0,"tid":"140258609698560","timestamp":1712329027} [GIN] 2024/04/05 - 16:57:07 | 200 | 13.526469804s | 127.0.0.1 | POST "/api/chat" >>> {"function":"update_slots","level":"INFO","line":1634,"msg":"slot released","n_cache_tokens":605,"n_ctx":2048,"n_past":604,"n_system_tokens":0,"slot_id":0,"task_id":0,"tid":"140258609698560","timestamp":1712329027,"truncated":false} ### What did you expect to see? normal behaviour (I've had no issues on a stand alone linux box (titan 16gb) or a Mac (m1, 16bg) ### Steps to reproduce ssh into the cluster sinteractive -p gpu --gres=gpu:1 --accel-bind=g --cpus-per-gpu=1 --mem-per-cpu=96G get to the right directory wget://https://github.com/ollama/ollama/releases/download/v0.1.30/ollama-linux-amd64 chmod +x ollama-linux-amd64 ./ollama-linux-amd64 serve& (this one gets right to the end and then stops...so I ^c to exit it, then) ./ollama-linux-amd64 run (insert model of choice) (and this runs fine...have to do it a few times cause pulling manifest hangs a few times with each run) then I get the normal ollama happy /? thing so I ask (if using the instruct) <s>[INST] please write a haiku about a llama[/INST] ### Are there any recent changes that introduced the issue? _No response_ ### OS Linux ### Architecture amd64 ### Platform _No response_ ### Ollama version 1.3 ### GPU Nvidia ### GPU info A100 80gb ### CPU Intel ### Other software nothing I can think of.

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I_kwDOJ0Z1Ps6elwvg

Add Nexusflow/Athene-V2-Chat and Nexusflow/Athene-V2-Agent

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NONE

They seem to just be based off Qwen 2.5 instruct this time

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I_kwDOJ0Z1Ps55UQ4_

CUDA error 2: out of memory (for a 33 billion param model, but I have 39GB of VRAM available across 4 GPUs)

closed

NONE

The model I'm trying to run is `deepseek-coder:33b` and `journalctl -u ollama` outputs: ``` Dec 11 18:31:37 x99 ollama[25964]: 2023/12/11 18:31:37 llama.go:292: 39320 MB VRAM available, loading up to 101 GPU layers Dec 11 18:31:37 x99 ollama[25964]: 2023/12/11 18:31:37 llama.go:421: starting llama runner Dec 11 18:31:37 x99 ollama[25964]: 2023/12/11 18:31:37 llama.go:479: waiting for llama runner to start responding Dec 11 18:31:37 x99 ollama[25964]: ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no Dec 11 18:31:37 x99 ollama[25964]: ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes Dec 11 18:31:37 x99 ollama[25964]: ggml_init_cublas: found 4 CUDA devices: Dec 11 18:31:37 x99 ollama[25964]: Device 0: NVIDIA GeForce GTX 1080 Ti, compute capability 6.1 Dec 11 18:31:37 x99 ollama[25964]: Device 1: NVIDIA GeForce GTX 1080 Ti, compute capability 6.1 Dec 11 18:31:37 x99 ollama[25964]: Device 2: NVIDIA GeForce GTX 1080 Ti, compute capability 6.1 Dec 11 18:31:37 x99 ollama[25964]: Device 3: NVIDIA GeForce GTX 1060 6GB, compute capability 6.1 Dec 11 18:31:39 x99 ollama[26042]: {"timestamp":1702290699,"level":"INFO","function":"main","line":2534,"message":"build info","build":375,"commit":"9656026"} Dec 11 18:31:39 x99 ollama[26042]: {"timestamp":1702290699,"level":"INFO","function":"main","line":2537,"message":"system info","n_threads":18,"n_threads_batch":-1,"total_threads":36,"system_info":"AVX = 1 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | "} Dec 11 18:31:39 x99 ollama[25964]: llama_model_loader: loaded meta data with 22 key-value pairs and 561 tensors from /usr/share/ollama/.ollama/models/blobs/sha256:137fe898f00f9b709b8ca96c549f64ad6a36ab85720cf10d3c24ac07389ab8fb (version GGUF V2) ---[snip]--- Dec 11 18:31:39 x99 ollama[25964]: llm_load_tensors: ggml ctx size = 0.21 MiB Dec 11 18:31:39 x99 ollama[25964]: llm_load_tensors: using CUDA for GPU acceleration Dec 11 18:31:39 x99 ollama[25964]: ggml_cuda_set_main_device: using device 0 (NVIDIA GeForce GTX 1080 Ti) as main device Dec 11 18:31:39 x99 ollama[25964]: llm_load_tensors: mem required = 124.24 MiB Dec 11 18:31:39 x99 ollama[25964]: llm_load_tensors: offloading 62 repeating layers to GPU Dec 11 18:31:39 x99 ollama[25964]: llm_load_tensors: offloading non-repeating layers to GPU Dec 11 18:31:39 x99 ollama[25964]: llm_load_tensors: offloaded 65/65 layers to GPU Dec 11 18:31:39 x99 ollama[25964]: llm_load_tensors: VRAM used: 17822.33 MiB Dec 11 18:31:43 x99 ollama[25964]: ................................................................................................... Dec 11 18:31:43 x99 ollama[25964]: llama_new_context_with_model: n_ctx = 16384 Dec 11 18:31:43 x99 ollama[25964]: llama_new_context_with_model: freq_base = 100000.0 Dec 11 18:31:43 x99 ollama[25964]: llama_new_context_with_model: freq_scale = 0.25 Dec 11 18:31:45 x99 ollama[25964]: llama_kv_cache_init: offloading v cache to GPU Dec 11 18:31:45 x99 ollama[25964]: llama_kv_cache_init: offloading k cache to GPU Dec 11 18:31:45 x99 ollama[25964]: llama_kv_cache_init: VRAM kv self = 3968.00 MiB Dec 11 18:31:45 x99 ollama[25964]: llama_new_context_with_model: kv self size = 3968.00 MiB Dec 11 18:31:45 x99 ollama[25964]: llama_build_graph: non-view tensors processed: 1430/1430 Dec 11 18:31:45 x99 ollama[25964]: llama_new_context_with_model: compute buffer total size = 1869.07 MiB Dec 11 18:31:46 x99 ollama[25964]: llama_new_context_with_model: VRAM scratch buffer: 1866.00 MiB Dec 11 18:31:46 x99 ollama[25964]: llama_new_context_with_model: total VRAM used: 23656.33 MiB (model: 17822.33 MiB, context: 5834.00 MiB) Dec 11 18:31:46 x99 ollama[25964]: CUDA error 2 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:7973: out of memory Dec 11 18:31:46 x99 ollama[25964]: current device: 0 Dec 11 18:31:47 x99 ollama[25964]: 2023/12/11 18:31:47 llama.go:436: 2 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:7973: out of memory Dec 11 18:31:47 x99 ollama[25964]: current device: 0 Dec 11 18:31:47 x99 ollama[25964]: 2023/12/11 18:31:47 llama.go:444: error starting llama runner: llama runner process has terminated ``` Ollama correctly identifies all 4 GPUs with a collective VRAM of `39320 MB VRAM available, loading up to 101 GPU layers` (first line of the logs). And then it proceeds to load the layers seemingly successfully, but then somehow an OOM error is triggered. How can I manually change the number of layers loaded to the GPU to debug this issue?

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I_kwDOJ0Z1Ps6NhpGW

openchat 8b

open

NONE

openchat/openchat-3.6-8b-20240522 https://huggingface.co./openchat/openchat_3.5

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I_kwDOJ0Z1Ps6jTU1D

No normalization option was provided when calling the embedding model

closed

NONE

### What is the issue? As the title says, I want to use ollama to call mxbai-embed-large:latest and output a normalized vector, but ollama does not seem to support normalized=true ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.5.1

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I_kwDOJ0Z1Ps531HVj

Terminal output issues on Windows

closed

NONE

I saw that #1262 was merged, so I pulled main and regenerated and built the binary. It runs great, and definitely uses the GPU, now: ``` ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9 ``` However, the terminal interface is broken in a way that I don't understand. Using Git Bash, it doesn't show output: ``` $ ./ollama.exe run orca-mini ``` Whereas if I [add `winpty`](https://stackoverflow.com/questions/32597209/python-not-working-in-the-command-line-of-git-bash): ``` $ winpty ./ollama.exe run orca-mini ←[?25l←[?25l←[?25h←[2K←[1G←[?25h←[?25l←[?25h←[?2004h>>> ←[38;5;245mSend a message (/? for help)←[28D←[0mwhy is the sky blue ←[Kwhy is the sky blue ←[?25l⠙ ←[?25h←[?25l←[2K←[1Gâ ¹ ←[?25h←[?25l←[2K←[1Gâ ¸ ←[?25h←[?25l←[?25l←[2K←[1G←[?25h←[2K←[1G←[?25h The←[?25l←[?25h sky←[?25l←[?25h appears←[?25l←[?25h blue←[?25l←[?25h b ecause←[?25l←[?25h of←[?25l←[?25h a←[?25l←[?25h phenomenon←[?25l←[?25h called←[?25l←[?25h Ray←[?25l←[?25hleigh←[?25l←[?25h scattering←[?25l←[?25h.←[?25l←[?25h When←[?25l ←[?25h sunlight←[?25l←[?25h enters←[?25l←[?25h the←[?25l←[?25h Earth←[?25l←[?25h'←[?25l←[?25hs←[?25l←[?25h atmosphere←[?25l←[?25h,←[?25l←[?25h it←[?25l←[?25h encounters← [?25l←[?25h tiny←[?25l←[?25h particles←[?25l←[?25h such←[?25l←[?25h as←[?25l←[?25h oxygen←[?25l←[?25h and←[?25l←[?25h nitrogen←[?25l←[?25h molecules←[?25l←[?25h.←[?25l←[ ?25h These←[?25l←[?25h particles←[?25l←[?25h sc←[?25l←[?25hatter←[?25l←[?25h the←[?25l←[?25h light←[?25l←[?25h in←[?25l←[?25h all←[?25l←[?25h directions←[?25l←[?25h,←[?2 5l←[?25h but←[?25l←[?25h they←[?25l←[?25h sc←[?25l←[?25hatter←[?25l←[?25h more←[?25l←[?25h easily←[?25l←[?25h towards←[?25l←[?25h the←[?25l←[?25h longer←[?25l←[?25h wave length←[?25l←[?25hs←[?25l←[?25h of←[?25l←[?25h light←[?25l←[?25h such←[?25l←[?25h as←[?25l←[?25h the←[?25l←[?25h v←[?25l←[?25hiolet←[?25l←[?25h and←[?25l←[?25h ind←[?25l ←[?25higo←[?25l←[?25h parts←[?25l←[?25h of←[?25l←[?25h the←[?25l←[?25h spectrum←[?25l←[?25h.←[?25l←[?25h This←[?25l←[?25h means←[?25l←[?25h that←[?25l←[?25h more←[?25l←[ ?25h blue←[?25l←[?25h light←[?25l←[?25h is←[?25l←[?25h scattered←[?25l←[?25h towards←[?25l←[?25h the←[?25l←[?25h observer←[?25l←[?25h,←[?25l←[?25h making←[?25l←[?25h the ←[?25l←[?25h sky←[?25l←[?25h appear←[?25l←[?25h more←[?25l←[?25h blue←[?25l←[?25h than←[?25l←[?25h it←[?25l←[?25h would←[?25l←[?25h be←[?25l←[?25h if←[?25l←[?25h all←[?2 5l←[?25h the←[?25l←[?25h colors←[?25l←[?25h of←[?25l←[?25h light←[?25l←[?25h were←[?25l←[?25h equally←[?25l←[?25h scattered←[?25l←[?25h.←[?25l←[?25h ←[?25l←[?25h>>> ←[38;5;245mSend a message (/? for help)←[28D←[0m←[K←[38;5;245mSend a message (/? for help)←[28D←[0m←[K ←[?2004l ``` Using Command Prompt doesn't fare much better: ``` ollama.exe run orca-mini ←[?25l←[?25l←[?25h←[2K←[1G←[?25h←[?25l←[?25h←[?2004h>>> ←[38;5;245mSend a message (/? for help)←[28D←[0m^D ←[K ←[?2004l ```

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I_kwDOJ0Z1Ps6UFj6L

Error 403 occurs when I call ollama's api

closed

NONE

### What is the issue? Prerequisite: Use the C++ interface of ipex-llm as ollama's acceleration backend. Then start the ollama server (port 127.0.0.1.11434). When you use the edge browser plug-in to access the api of ollama, error 403 occurs  ### OS Windows ### GPU Intel ### CPU Intel ### Ollama version _No response_

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I_kwDOJ0Z1Ps55-9Ux

why am I keep failing in request from my server?

closed

NONE

Hello :) I typed ollama serve then with port forwarding, xxx.xxx.xxx(myserver) : 5050 then there is message saying Ollama is running so , on postman, I tried to send prompt to receive answer from ollama. <img width="746" alt="스크린샷 2023-12-18 오후 9 18 12" src="https://github.com/jmorganca/ollama/assets/20656932/b3dc3c78-a07b-452e-8e58-4d02 <img width="1156" alt="스크린샷 2023-12-18 오후 9 18 40" src="https://github.com/jmorganca/ollama/assets/20656932/7c6cb9e0-1c02-4aac-ae6a-d9dbbbbd1330"> 35d17eaf"> but cause error... what maybe the problem? also, I try use it in my node js server with below code <img width="842" alt="스크린샷 2023-12-18 오후 9 19 20" src="https://github.com/jmorganca/ollama/assets/20656932/13e7ee2f-f027-45f4-baaa-f472656f9c7b"> it just return 500 error. <img width="717" alt="스크린샷 2023-12-18 오후 9 19 42" src="https://github.com/jmorganca/ollama/assets/20656932/bbd6db26-3fda-44af-8698-5ea9b4690c1d"> <img width="565" alt="스크린샷 2023-12-18 오후 9 31 08" src="https://github.com/jmorganca/ollama/assets/20656932/cd6e673f-b389-4731-bc52-ce17d59f102d"> what may be the problem?

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PR_kwDOJ0Z1Ps51TyoM

Disable mmap by default for Windows ROCm

closed

NONE

The same as with CUDA, disabling mmap when using ROCm on windows seems to speed up model load times significantly. I get a >2x speedup in model load times on my 7900xtx when disabling mmap.

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PR_kwDOJ0Z1Ps53C8lu

server: fix json marshalling of downloadBlobPart

closed

CONTRIBUTOR

The json marshalling of downloadBlobPart was incorrect and racey. This fixes it by implementing customer json marshalling for downloadBlobPart which correctly handles serialization of shared memory.

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PR_kwDOJ0Z1Ps5z10qG

Draft: Support Moore Threads GPU

closed

CONTRIBUTOR

Moore Threads, a cutting-edge GPU startup, introduces MUSA (Moore Threads Unified System Architecture) as its foundational technology. This pull request marks the initial integration of MT GPU support into Ollama, leveraging MUSA's capabilities to enhance LLM inference performance. I am also working on integrating MTGPU support into llama.cpp and am optimistic that there will be no significant blocker issues. Update 0703: I was able to get it working on MTGPU. <img width="957" alt="Screenshot 2024-07-03 at 15 58 51" src="https://github.com/ollama/ollama/assets/2831050/1237baba-71be-4335-853c-9120bcdd1f8d">

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I_kwDOJ0Z1Ps6Cin2G

Suppressing output of all the metadata.

closed

NONE

### What is the issue? I want to use Ollama to serve a local LLM with OpenAI API to allow Pythogora/gpt-pilot to interact with it. The back end constantly prints out the crap below: ```bash {"function":"launch_slot_with_data","id_slot":0,"id_task":2842,"level":"INFO","line":1002,"msg":"slot is processing task","tid":"139720604485376","timestamp":1710547752} {"function":"update_slots","id_slot":0,"id_task":2842,"level":"INFO","line":1916,"msg":"kv cache rm [p0, end)","p0":8,"tid":"139720604485376","timestamp":1710547752} {"function":"update_slots","id_slot":0,"id_task":2842,"level":"INFO","line":1916,"msg":"kv cache rm [p0, end)","p0":520,"tid":"139720604485376","timestamp":1710547760} {"function":"update_slots","id_slot":0,"id_task":2842,"level":"INFO","line":1916,"msg":"kv cache rm [p0, end)","p0":1032,"tid":"139720604485376","timestamp":1710547767} {"function":"update_slots","id_slot":0,"id_task":2842,"level":"INFO","line":1916,"msg":"kv cache rm [p0, end)","p0":1544,"tid":"139720604485376","timestamp":1710547775} DONE{"function":"print_timings","id_slot":0,"id_task":2842,"level":"INFO","line":310,"msg":"prompt eval time = 26782.10 ms / 1736 tokens ( 15.43 ms per token, 64.82 tokens per second)","n_prompt_tokens_processed":1736,"n_tokens_second":64.81942506738177,"t_prompt_processing":26782.095,"t_token":15.427474078341014,"tid":"139720604485376","timestamp":1710547780} {"function":"print_timings","id_slot":0,"id_task":2842,"level":"INFO","line":326,"msg":"generation eval time = 612.50 ms / 3 runs ( 204.17 ms per token, 4.90 tokens per second)","n_decoded":3,"n_tokens_second":4.897951187018471,"t_token":204.167,"t_token_generation":612.501,"tid":"139720604485376","timestamp":1710547780} {"function":"print_timings","id_slot":0,"id_task":2842,"level":"INFO","line":337,"msg":" total time = 27394.60 ms","t_prompt_processing":26782.095,"t_token_generation":612.501,"t_total":27394.596,"tid":"139720604485376","timestamp":1710547780} {"function":"update_slots","id_slot":0,"id_task":2842,"level":"INFO","line":1611,"msg":"slot released","n_cache_tokens":1746,"n_ctx":2048,"n_past":1746,"n_system_tokens":0,"tid":"139720604485376","timestamp":1710547780,"truncated":false} {"function":"update_slots","level":"INFO","line":1637,"msg":"all slots are idle","tid":"139720604485376","timestamp":1710547780} {"function":"update_slots","level":"INFO","line":1637,"msg":"all slots are idle","tid":"139720604485376","timestamp":1710547780} [GIN] 2024/03/15 - 20:09:40 | 200 | 27.405338872s | 127.0.0.1 | POST "/v1/chat/completions" ``` ### What did you expect to see? I want some means to see NO metadata at all. I just to see the prompt text and the answer text and none of the spurious stuff. ### Steps to reproduce It is obvious "ollama run somemodel" ### Are there any recent changes that introduced the issue? _No response_ ### OS _No response_ ### Architecture _No response_ ### Platform _No response_ ### Ollama version _No response_ ### GPU _No response_ ### GPU info _No response_ ### CPU _No response_ ### Other software _No response_

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I_kwDOJ0Z1Ps6YOeg2

Option to Override a Model's Memory Requirements

closed

NONE

I was trying to load the 70b Llama3 model and Ollama says I need 33.6 GB of 30.5 GB ram. I believe this is a safety thing Meta put into the model, so I want to have the ability to override this and attempt to run it on lower amounts of memory. I know this will likely dip into swap/page file space, possibly even causing kernel panics and BSODs. But I still want to make the attempt. _Computer, disable holodeck safety protocols. Authorization Picard-4-7-Alpha-Tango._

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PR_kwDOJ0Z1Ps5VtvIK

fix stream errors

closed

CONTRIBUTOR

once the stream is created, it's too late to update response headers (i.e. status code). any and all errors must be returned by the stream

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PR_kwDOJ0Z1Ps59-RSp

llama: add compiler tags for cpu features

closed

COLLABORATOR

Replaces #7009 now on main Support local builds with customized CPU flags for both the CPU runner, and GPU runners. Some users want no vector flags in the GPU runners. Others want ~all the vector extensions enabled. Each runner we add to the official build adds significant overhead (size and build time) so this enhancement makes it much easier for users to build their own customized version if our default runners CPU: [none,avx,avx2] and GPU:[avx] don't address their needs. This PR does not wire up runtime discovery of the requirements, so will only be suitable for adding additional vector flags to GPU runners for now. I'll follow up in the future with support for GPU runners without any vector flags along with docs.

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I_kwDOJ0Z1Ps5-LQV8

Feature Request - Support for ollama Keep alive

closed

NONE

there is a new api parameter for keeping the model loaded. it would be great to have it as a passable parameter in the modelfile.

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I_kwDOJ0Z1Ps6QEELq

Mistral Nemo Please!

closed

NONE

https://mistral.ai/news/mistral-nemo/

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I_kwDOJ0Z1Ps54buQc

Beam search (best of) for completion API

open

NONE

Beam search is a sampling mechanism by which we maximize probability of not just the next token, but the entire completion. While it can be ignored for simpler uses, any form of reasoning, especially with a tiny model, requires beam search to backtrack from incorrect steps. llama.cpp already supports beam search, so it should be trivial to implement.

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I_kwDOJ0Z1Ps6dkflc

LLaMa 3.2 90B on multi GPU crashes

closed

NONE

### What is the issue? Hello! My problem may be similar to issue 7568. I think there is a problem with the distribution of the LLaMa 3.2 90B model across multiple GPUs. When it runs on a single GPU (quantized), it works. But when it runs on multiple GPUs, it crashes. In my server running Linux Debian Bookworm and having 4x Nvidia H100 NVL, I can easily run a 4-bit quantized version of LLaMa 3.2 90B model. Here is the call of a "ollama run" in this case : $ ollama run llama3.2-vision:90b-instruct-q4_K_M >>> What do you see in this picture ? ./TeddyBear.jpg Added image './TeddyBear.jpg' The image shows a teddy bear. And here is the ollama serve 0.4.0 output : [OLLAMA_SERVE_llama3.2-vision_90b-instruct-q4_K_M.log](https://github.com/user-attachments/files/17676883/OLLAMA_SERVE_llama3.2-vision_90b-instruct-q4_K_M.log) But when I run a 8 bits quantized version model (dispatched on my 4x Nvidia H100), here is what I get : $ ollama run llama3.2-vision:90b-instruct-q8_0 >>> What do you see in this picture ? ./TeddyBear.jpg Added image './TeddyBear.jpg' Error: POST predict: Post "http://127.0.0.1:40459/completion": EOF And here is the ollama serve 0.4.0 output I get : [OLLAMA_SERVE_llama3.2-vision_90b-instruct-q8_0.log](https://github.com/user-attachments/files/17676927/OLLAMA_SERVE_llama3.2-vision_90b-instruct-q8_0.log) The program crashes with a Segfault signal (as in ticket 7568 mentioned earlier). Can you take a look at this please? Thanks in advance! ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.4.0

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I_kwDOJ0Z1Ps57Lets

Readme refers to 404 docker documentation

closed

NONE

The main [readme](https://github.com/jmorganca/ollama/blob/main/docs/README.md) refers to https://github.com/jmorganca/ollama/blob/main/docs/docker.md which gives a 404. Is docker still supported?

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PR_kwDOJ0Z1Ps5vUQLp

removed inconsistent punctuation

closed

CONTRIBUTOR

Removed the period in `ollama serve -h` Resolves https://github.com/ollama/ollama/issues/4410

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I_kwDOJ0Z1Ps6NVDY3

Support Multiple Types for OpenAI Completions Endpoint

open

CONTRIBUTOR

Allow v1/completions to handle []string, []int and [][]int, in addition to just a string

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I_kwDOJ0Z1Ps6GOQqL

Please accept slow network connections when loading models

closed

NONE

### What is the issue? Downloading of models on slow networks stops too frequently (base) igor@macigor ~ % ollama run llava:7b pulling manifest pulling 170370233dd5... 23% ▕███ ▏ 959 MB/4.1 GB 882 KB/s 59m29s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/17/170370233dd5c5415250a2ecd5c71586352850729062ccef1496385647293868/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240418%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240418T205718Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=a3c02ead244fe32e877db7493a63034cfd737643c3fb8ab4bbc86d75c768d3f1": net/http: TLS handshake timeout (base) igor@macigor ~ % ollama run llava:7b pulling manifest pulling 170370233dd5... 50% ▕███████ ▏ 2.0 GB/4.1 GB 146 KB/s 3h56m Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/17/170370233dd5c5415250a2ecd5c71586352850729062ccef1496385647293868/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240418%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240418T213225Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=0cdc6db7e37852c2302d7a2cebb0efeee5136292fddcdb90654a3080224be9c8": read tcp 192.168.1.82:50082->104.18.9.90:443: read: connection reset by peer (base) igor@macigor ~ % ollama run llava:7b pulling manifest pulling 170370233dd5... 77% ▕████████████ ▏ 3.2 GB/4.1 GB 1.5 MB/s 10m14s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/17/170370233dd5c5415250a2ecd5c71586352850729062ccef1496385647293868/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240418%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240418T214548Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=83897005cf68db7e5276cc7a6329e8eac7bad00ff655d8cfd87412e665328af0": net/http: TLS handshake timeout (base) igor@macigor ~ % ollama run llava:7b pulling manifest pulling 170370233dd5... 100% ▕████████████████▏ 4.1 GB pulling 72d6f08a42f6... 100% ▕████████████████▏ 624 MB pulling 43070e2d4e53... 100% ▕████████████████▏ 11 KB pulling c43332387573... 100% ▕████████████████▏ 67 B pulling ed11eda7790d... 100% ▕████████████████▏ 30 B pulling 7c658f9561e5... 100% ▕████████████████▏ 564 B verifying sha256 digest writing manifest removing any unused layers success >>> /exit (base) igor@macigor ~ % ollama run llama3 pulling manifest pulling 00e1317cbf74... 10% ▕█ ▏ 473 MB/4.7 GB 1.4 MB/s 49m53s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/00/00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240418%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240418T224502Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=7a675059c277b61e2e37ffa05bebcfdc5ef3b141bf9f794b9ff3797e926c6429": net/http: TLS handshake timeout (base) igor@macigor ~ % ollama run llama3 pulling manifest pulling 00e1317cbf74... 30% ▕████ ▏ 1.4 GB/4.7 GB 1.3 MB/s 41m46s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/00/00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240418%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240418T225758Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=6e860ac70cb81d1a0c7b2830264bec22398ac377382009088111f3117abd88bb": net/http: TLS handshake timeout (base) igor@macigor ~ % ollama run llama3 pulling manifest pulling 00e1317cbf74... 47% ▕███████ ▏ 2.2 GB/4.7 GB 1.3 MB/s 31m49s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/00/00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240419%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240419T003938Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=4c514b8fd50d56f95adcd60d9efac7f4f2177b0bbbe5e5200f254ac3b1a804c1": read tcp [2a01:cb20:4014:3400:ad09:e45b:4793:12ea]:57813->[2606:4700::6812:95a]:443: read: connection reset by peer (base) igor@macigor ~ % ollama run llama3 pulling manifest pulling 00e1317cbf74... 61% ▕█████████████████████████████████████████████ ▏ 2.9 GB/4.7 GB 1.5 MB/s 19m39s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/00/00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240419%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240419T004701Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=a01e769aa10f27c7020d029aa6cc7814ee96b8f084c27013e3646bac013c6aa0": net/http: TLS handshake timeout (base) igor@macigor ~ % ollama run llama3 pulling manifest pulling 00e1317cbf74... 74% ▕███████████████████████████████████████████████████████ ▏ 3.4 GB/4.7 GB 1.1 MB/s 18m44s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/00/00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240419%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240419T005553Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=3c756468c56b53adfe4f4be731f3daf7dc04b425686c6df2f1d567195f4f7f6f": read tcp [2a01:cb20:4014:3400:ad09:e45b:4793:12ea]:59735->[2606:4700::6812:85a]:443: read: connection reset by peer (base) igor@macigor ~ % ollama run llama3 pulling manifest pulling 00e1317cbf74... 92% ▕█████████████████████████████████████████████████████████████████████ ▏ 4.3 GB/4.7 GB 1.4 MB/s 4m20s Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/00/00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240419%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240419T011033Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=d42500def336a35289b51f066a0e9cac370e8f035b9403b1c4bcf18508c8c409": read tcp 192.168.1.82:61035->104.18.8.90:443: read: connection reset by peer (base) igor@macigor ~ % ollama run llama3 pulling manifest pulling 00e1317cbf74... 100% ▕███████████████████████████████████████████████████████████████████████████▏ 4.7 GB pulling 4fa551d4f938... 100% ▕███████████████████████████████████████████████████████████████████████████▏ 12 KB pulling 8ab4849b038c... 100% ▕███████████████████████████████████████████████████████████████████████████▏ 254 B pulling c0aac7c7f00d... 100% ▕███████████████████████████████████████████████████████████████████████████▏ 128 B pulling db46ef36ef0b... 100% ▕███████████████████████████████████████████████████████████████████████████▏ 483 B verifying sha256 digest writing manifest removing any unused layers success >>> Sen ### OS macOS ### GPU _No response_ ### CPU Apple ### Ollama version 0.1.32

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I_kwDOJ0Z1Ps6a6Ijv

You want to be able to customize the OLLAMA installation directory

closed

NONE

Sometimes my user directory space is not much, want to install software to other drive. This solves both the space issue and customizes the management of your own installed software. I tried to use the OLLAMA variable setting, but it didn't work and the OLLAMA was installed on the C drive. It's even better if you can customize the location of your software installation.

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PR_kwDOJ0Z1Ps50mVFp

llm: add `-DBUILD_SHARED_LIBS=off` to common cpu cmake flags

closed

MEMBER

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I_kwDOJ0Z1Ps6fkteu

Request: Nexa AI Omnivision

open

NONE

Here is the link to Nexa AI's model. (I haven't checked over it to make sure it's super reputable though) https://huggingface.co./NexaAIDev/omnivision-968M

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I_kwDOJ0Z1Ps6HJnuX

could not connect to ollama app

closed

NONE

### What is the issue? could not connect to ollama app，is it running？ it always happen even reinstall ollama ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version _No response_

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https://github.com/ollama/ollama/pull/462

PR_kwDOJ0Z1Ps5ZbW3F

remove marshalPrompt which is no longer needed

closed

CONTRIBUTOR

llama.cpp server handles truncating input

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I_kwDOJ0Z1Ps52L6Vi

Fail to load Custom Models

closed

NONE

Hi I want to load a custom gguf model [TheBloke/deepseek-coder-6.7B-instruct-GGUF](https://huggingface.co./TheBloke/deepseek-coder-6.7B-instruct-GGUF) ModelFile is: ``` FROM ./deepseek-coder-6.7b-instruct.Q4_K_M.gguf ``` But when I do build, it reports a error for me. ``` % ollama create amodel -f ./Modelfile parsing modelfile looking for model ⠋ creating model layer Error: invalid version ``` And I do this on my old mac(MacBook Air (13-inch, Early 2015)) Could you help me how to solve this?

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I_kwDOJ0Z1Ps6BvfbI

Scoop repo, NIX repo & Debian repo

closed

NONE

Please try and get Ollama into the Windows Scoop package repo and try to get Ollama into the Linux Nix repo and Debian repo.

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PR_kwDOJ0Z1Ps5eYgin

fix log

closed

CONTRIBUTOR

if there's a remainder, the log line will show the remainder instead of the actual size

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I_kwDOJ0Z1Ps6KGbvw

Getting Weird Response

closed

NONE

### What is the issue? I am using ollama on Apple Mac M2 Ultra. i am getting same problem for past two days. i delete all my models, i uninstalled ollama and reinstall again and sometimes it will work and most of the times it will get some weird response.  ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.1.38

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https://github.com/ollama/ollama/pull/2355

PR_kwDOJ0Z1Ps5l9foV

Fit and finish, clean up cruft on uninstall

closed

COLLABORATOR

Better job cleaning up, logging improvements, and now spawn a powershell window on first use to help users with their first run

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I_kwDOJ0Z1Ps6LP5xi

llama runner process has terminated: exit status 127

closed

NONE

### What is the issue? When i try running ollama run codestral or any other LLM, I keep getting the same status 127 error! Is there a way to fix this? I'm running EndeavourOs Arch linux. ### OS Linux ### GPU AMD ### CPU AMD ### Ollama version 0.1.41

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I_kwDOJ0Z1Ps6QNjZU

support for arm linux

closed

CONTRIBUTOR

Support for arm linux build so it can be installed in arm laptops which will be coming with new snapdragon x elite cpus with a good npu

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I_kwDOJ0Z1Ps54hRy9

Quitting ollama completely

closed

NONE

`ollama` is still running in the background after complete command. If user does not kill backgroud process, it can wastes a lot of energy. ``` $ nvidia-smi -q -d MEMORY ==============NVSMI LOG============== Timestamp : Sat Dec 2 17:39:41 2023 Driver Version : 535.129.03 CUDA Version : 12.2 Attached GPUs : 1 GPU 00000000:01:00.0 FB Memory Usage Total : 8192 MiB Reserved : 218 MiB Used : 9 MiB Free : 7964 MiB BAR1 Memory Usage Total : 8192 MiB Used : 3 MiB Free : 8189 MiB Conf Compute Protected Memory Usage Total : 0 MiB Used : 0 MiB Free : 0 MiB $ ollama run codellama:13b-python "compute gcd of a and b" ''' def gcd(a,b): if b == 0: return a # recursively call with the remainder of a and b until there is no remainder. return gcd(b,a % b) $ nvidia-smi -q -d MEMORY ==============NVSMI LOG============== Timestamp : Sat Dec 2 17:40:26 2023 Driver Version : 535.129.03 CUDA Version : 12.2 Attached GPUs : 1 GPU 00000000:01:00.0 FB Memory Usage Total : 8192 MiB Reserved : 218 MiB Used : 6447 MiB Free : 1525 MiB BAR1 Memory Usage Total : 8192 MiB Used : 5 MiB Free : 8187 MiB Conf Compute Protected Memory Usage Total : 0 MiB Used : 0 MiB Free : 0 MiB ```

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I_kwDOJ0Z1Ps6n_8Ah

deepseek-r1:671b Q4_K_M: error="model requires more system memory (446.3 GiB) than is available

open

NONE

Hey Ollama community, I’m reaching out for some advice on running the DeepSeek-R1 671B model with Q4 quantization on my current setup, which has 40GB of RAM. I understand that this model employs a Mixture of Experts (MoE) architecture, meaning that during inference, only a subset of the model’s parameters (approximately 37 billion) is active at any given time. This design should, in theory, reduce the immediate memory requirements compared to loading all 671 billion parameters simultaneously. Based on calculations, the active 37B parameters in Q4 quantization should only require around **18.5GB of RAM**, with an additional **20–30% memory overhead**, resulting in a total memory requirement of **~25–30GB**. In contrast, the Q8_0 version (8-bit quantization) would require approximately **37GB + overhead**, totaling around **50GB**. Even the full 671B parameters in Q4 quantization should only need around **325GB of RAM** if loaded entirely, and Q8_0 would need **650GB**. Given these figures, my expectation was that loading just the active 37B subset in Q4 should work within reasonable hardware configurations. However, when I attempt to load the model using Ollama, I encounter an error indicating that the model requires more system memory (446.3 GiB) than is available (37.3 GiB). This is perplexing, given the MoE architecture’s supposed efficiency and the Q4 quantization. I came across a discussion where a user successfully ran the Q8_0 GGUF version of DeepSeek-R1 671B on a CPU-only system with 256GB of RAM. They noted that with a context length of 32,092 tokens, the system utilized around 220GB of RAM, emphasizing that the KV cache can consume more RAM than the model itself. Reference: https://huggingface.co./deepseek-ai/DeepSeek-R1/discussions/19 Based on this, I would expect that running the Q4 quantized model with a context length of 32,092 tokens would require less memory than the Q8 version, potentially making it feasible to run on a system with 192GB of RAM(What I'm planning to upgrade to). However, the error message I received suggests otherwise, indicating a requirement of 446.3 GiB of system memory. Thank you for your help. Error Logs: ``` 2025/01/29 14:31:32 routes.go:1187: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:5m0s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[* http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://*] OLLAMA_SCHED_SPREAD:false ROCR_VISIBLE_DEVICES: http_proxy: https_proxy: no_proxy:]" time=2025-01-29T14:31:32.660Z level=INFO source=images.go:432 msg="total blobs: 11" time=2025-01-29T14:31:32.680Z level=INFO source=images.go:439 msg="total unused blobs removed: 0" time=2025-01-29T14:31:32.699Z level=INFO source=routes.go:1238 msg="Listening on [::]:11434 (version 0.5.7-0-ga420a45-dirty)" time=2025-01-29T14:31:32.701Z level=INFO source=routes.go:1267 msg="Dynamic LLM libraries" runners="[cuda_v12_avx cpu cpu_avx cpu_avx2 cuda_v11_avx]" time=2025-01-29T14:31:32.701Z level=INFO source=gpu.go:226 msg="looking for compatible GPUs" time=2025-01-29T14:31:32.729Z level=WARN source=amd_linux.go:61 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory" time=2025-01-29T14:31:32.730Z level=INFO source=amd_linux.go:404 msg="no compatible amdgpu devices detected" time=2025-01-29T14:31:32.730Z level=INFO source=gpu.go:392 msg="no compatible GPUs were discovered" time=2025-01-29T14:31:32.730Z level=INFO source=types.go:131 msg="inference compute" id=0 library=cpu variant=avx compute="" driver=0.0 name="" total="39.2 GiB" available="37.9 GiB" time=2025-01-29T14:33:04.221Z level=INFO source=server.go:104 msg="system memory" total="39.2 GiB" free="37.3 GiB" free_swap="0 B" time=2025-01-29T14:33:04.223Z level=WARN source=server.go:136 msg="model request too large for system" requested="446.3 GiB" available=40099082240 total="39.2 GiB" free="37.3 GiB" swap="0 B" time=2025-01-29T14:33:04.223Z level=INFO source=sched.go:428 msg="NewLlamaServer failed" model=/root/.ollama/models/blobs/sha256-9801e7fce27dbf3d0bfb468b7b21f1d132131a546dfc43e50518631b8b1800a9 error="model requires more system memory (446.3 GiB) than is available (37.3 GiB)" [GIN-debug] [WARNING] ```

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I_kwDOJ0Z1Ps6Xd-9b

Mixture of Agents for Ollama

closed

CONTRIBUTOR

The [Mixture of Agents (MoA)](https://arxiv.org/abs/2406.04692) is an innovative approach to leveraging the collective strengths of multiple language models to enhance overall performance and capabilities of one main model (aggregator). By combining outputs from various models, each potentially excelling in different aspects or domains, this approach has demonstrated significant improvements in model performance, even outperforming GPT-4o on certain benchmarks.  This proposal aims to bring similar capabilities to Ollama, allowing users to define and utilize multiple agent models within a single inference pipeline. It suggests extending Ollama's Modelfile vocabulary to support a MoA architecture. The feature would enable users to: 1. Define multiple "reference" models, each potentially specialized for different tasks or domains. 2. Specify an "aggregator" model that synthesizes the outputs from these referenced models. 3. Create sophisticated inference pipelines that can adapt to various types of queries or tasks. Expanding Ollama's capabilities with MoA would make Ollama more flexible and powerful, and offer enhanced model performance. This feature would significantly , especially beneficial in scenarios where diverse expertise or multimodal processing is required. ## Proposed Syntax Extensions 1. **`REFERENCE`:** Define a reference model to be used as an agent. These directives must be placed before `FROM`, so it can be referenced in the system prompt of the aggregator model. ``` REFERENCE <model_name> AS <expert_alias> ``` - All configurations following this statement until the next `REFERENCE` or `FROM` apply to this agent model. 2. **`FROM`:** Defines the aggregator model, equal to its current use in Modelfiles. Must come after any `REFERENCE` directive. - All configurations following this statement apply to the aggregator model. 3. **`SYSTEM`:** Defines the system prompt of the reference or aggregator model, equal to its current use in Modelfiles. 4. **`TEMPLATE --type=user-message`:** Defines a template for user messages given to the reference or aggregator model. - This proposal introduces new template variables for this case to access agent outputs. - Available template variables for `TEMPLATE --type=user-message`: - `{{ .Ref.<expert_alias> }}` ## Example Usage ```dockerfile # Define first reference model REFERENCE qwen2.5 AS companion PARAMETER temperature 0.3 PARAMETER num_predict 512 SYSTEM """ You are a companion AI expert. Provide friendly and supportive responses. """ # Define second reference model (multimodal) REFERENCE llava AS sight PARAMETER temperature 0.4 PARAMETER num_predict 256 SYSTEM """ You are a visual analysis expert. Describe and analyze visual elements. """ # Aggregator model configuration FROM llama3.1 PARAMETER temperature 0.5 PARAMETER num_ctx 4096 TEMPLATE --type=user-message """ You have been provided with a set of responses from various agent LLMs to the latest user query. Your task is to synthesize these responses into a single, high-quality response. It is crucial to critically evaluate the information provided in these responses, recognizing that some of it may be biased or incorrect. Your response should not simply replicate the given answers but should offer a refined, accurate, and comprehensive reply to the instruction. Ensure your response is well-structured, coherent, and adheres to the highest standards of accuracy and reliability. Responses from agents: Companion: {{ .Ref.companion }} Sight: {{ .Ref.sight }} """ ``` ## Benefits 1. Enhanced Model Capabilities: Leverage strengths of multiple models for more comprehensive responses. 2. Flexibility: Easy configuration and deployment of complex model ensembles through declarative syntax. 3. Improved Context-Awareness: Utilize specialized models for different aspects of queries. 4. Familiar Syntax: Builds upon existing Modelfile conventions, making it intuitive for Ollama and Docker users. ## Potential Use Cases 1. Multimodal Processing: Combine text and image/video/voice analysis for richer understanding. 2. Enhanced Question-Answering, Fact-Checking, and Research: Use specialized experts for different knowledge domains, aggregating information from multiple specialized sources. 3. Context-Aware Conversational Agents: Dynamically adapt responses based on conversational context, or use different experts for style, content, and personality aspects. ## See also - [Blog Post about Together MoA](https://www.together.ai/blog/together-moa) - [Together MoA Documentation](https://docs.together.ai/docs/mixture-of-agents) - [Together MoA GitHub Repo](https://github.com/togethercomputer/MoA)

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I_kwDOJ0Z1Ps6BDE-x

Fail to load dynamic library - unicode character path

closed

NONE

Error: Unable to load dynamic library: Unable to load dynamic server library: �Ҳ���ָ����ģ�顣

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PR_kwDOJ0Z1Ps5qDq2r

do not prompt to move the CLI on install flow if already installed

open

CONTRIBUTOR

If Ollama is installed via brew or the user wishes to manage their path manually they will be prompted to install the CLI when opening the Ollama Mac app. This change attempts to check if Ollama is already set in the path, and if it is found the user is not prompted to link the executable to `/usr/local/bin/ollama`. - rather than checking if the symlink is set try to spawn common shells and see if ollama is available within them - make init async to allow for spawning shells - check for `step` param in installation UI to bypass the `move to applications` prompt - flatten `init()` conditional check to link ollama in `/usr/local/bin/ollama` resolves #283 resolves #3186

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PR_kwDOJ0Z1Ps5xqIVb

Error handling load_single_document() in ingest.py

closed

CONTRIBUTOR

load_single_document() handles - corrupt files - empty (zero byte) files - unsupported file extensions

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PR_kwDOJ0Z1Ps5kOMkJ

Fix show parameters

closed

CONTRIBUTOR

The ShowParameters call was converting some floats into ints. This simplifies the code and adds a unit test.

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I_kwDOJ0Z1Ps531E94

Specify where to download and look for models

closed

NONE

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I_kwDOJ0Z1Ps6ZxJdJ

Qwen 2.5 72B missing stop parameter

open

NONE

### What is the issue? Often doesn't stop generating...  PARAMETER stop <|endoftext|> seems to be missing in the model configuration. Adding it solved the problem. ### OS Ubuntu 22.04.5 LTS (but ollama runs in official docker container) ### GPU 2 x RTX 4090 ### CPU AMD Ryzen 9 7950X ### Ollama version 0.3.12

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I_kwDOJ0Z1Ps6Pxfdh

Installation on Linux fails because /usr/share/ollama does not exist.

closed

NONE

### What is the issue? When you install using the install script on openSUSE Tumbleweed, the script fails because the adduser command with the -m does not create the directory /usr/share/ollama, it merely assigns that directory to the ollama user. I had Claude Sonnet go over the install script line by line explaining to me what it does. Here is what Claude has to say about that line in response to this query: Correct me if I'm wrong, but this line here: $SUDO useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama creates the account with the home directory at /usr/share/ollams. Where in the script is that directory created? Claude Sonnet: Claude 3.5 Sonnet You're absolutely correct, and I apologize for the oversight in my previous explanation. You've made a very astute observation. Let's clarify this: The line you mentioned: bash Copy code $SUDO useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama Indeed creates the 'ollama' user with the home directory set to /usr/share/ollama. You're right to question where this directory is created. In the original script, there isn't an explicit command to create this directory. The -m option in the useradd command is supposed to create the home directory if it doesn't exist, but it's generally a good practice to ensure the parent directory exists. To improve this, we should add a line to create the directory before creating the user. Here's how we could modify that section: bash Copy code if ! id ollama >/dev/null 2>&1; then status "Creating ollama user..." $SUDO mkdir -p /usr/share/ollama $SUDO useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama fi This change ensures that the /usr/share/ollama directory exists before we try to create the user with that home directory. Thank you for catching this. It's an important detail that could potentially cause issues if the parent directory /usr/share didn't exist or wasn't writable. Your attention to detail helps improve the robustness of the script. ### OS Linux ### GPU AMD ### CPU AMD ### Ollama version v0.2.5

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I_kwDOJ0Z1Ps6AUD2-

How to improve ollama performance

closed

NONE

current model params : FROM llama2:13b-chat PARAMETER temperature 0.2 PARAMETER num_ctx 4096 PARAMETER num_thread 16 PARAMETER use_mmap False System config : Ram 108 GB T4 graphics card 16 gb  Also hardly any ram is being used. Using ollama python bindings to get the result but due to some params issue not getting the result as expected. What am i missing here ?

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I_kwDOJ0Z1Ps6B9aRi

Additional package manager support

open

MEMBER

This is an issue that encompasses supporting other package managers: - [ ] MacPorts - [ ] Apt - [ ] Scoop - [ ] Nix

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PR_kwDOJ0Z1Ps6EQl5A

Update readmes for structured outputs

closed

CONTRIBUTOR

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I_kwDOJ0Z1Ps6NWiXu

How to use the .mf model configuration file to register a customize vision-language model in Ollama

open

NONE

This is my .mf model configuration file: FROM /root/llava-llama-3-8b-v1_1-gguf/llava-llama-3-8b-v1_1-f16.gguf TEMPLATE """ {{ if .System }}<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> {{ .Response }}<|eot_id|> """ PARAMETER stop "<|system|>" PARAMETER stop "<|user|>" PARAMETER stop "<|assistant|> Ollama can recognize our customized .gguf model and display a model option in open-webui. However, the loaded model cannot understand the content of the input image and randomly says things that are completely unrelated to the image. What could be the cause of this problem? Thank you in advance! @jmorganca

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I_kwDOJ0Z1Ps5tNBP2

Streaming llama output

closed

NONE

@jmorganca @mxyng Testing llama2 output after building and serving the model and with the example `why the sky is blue` I noticed that the streaming does not work correctly. Using the curl or python requests either on ubuntu 22.04 I got something like: {"model":"llama2","created_at":"2023-08-01T22:02:11.894420812Z","response":" sc","done":false} {"model":"llama2","created_at":"2023-08-01T22:02:12.151293915Z","response":"at","done":false} {"model":"llama2","created_at":"2023-08-01T22:02:12.409555353Z","response":"ters","done":false} Is there anyway to disable streaming?

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I_kwDOJ0Z1Ps6cWQxI

Optimizing Single Inference Performance on Distributed GPUs with Ollama’s Parallel Inference

closed

NONE

Hey guys, I have a server with dual A100 GPUs and a server with a single V100 GPU. Knowing the IP addresses, ports, and passwords of both servers, I want to use Ollama’s parallel inference functionality to perform a single inference request on the llama3.1-70B model. How can I achieve optimal performance for a single request when using Ollama for inference? Do I need to use MPI or other distributed methods? Will this make the model inference faster? Thanks.

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I_kwDOJ0Z1Ps6AT6vo

Using embedding and llm at the same time

closed

NONE

I just have the question: can I use embedding and llm at the same time without it loading the embedding and unloading the llm? I wanted to use it in llama index and want to know if this works before trying to make it work

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PR_kwDOJ0Z1Ps55oM7z

add safetensors to the modelfile docs

closed

CONTRIBUTOR

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I_kwDOJ0Z1Ps6HtE1h

`ollama create` tries to pull model when using quotes in `FROM` line

closed

NONE

### What is the issue? Steps to reproduce: 1. Download and install [Ollama v0.1.33 from release page](https://github.com/ollama/ollama/releases/tag/v0.1.33) 2. Download any gguf file and make a modelfile for the gguf file (for example, im using hermes 2 pro): Modelfile ``` FROM "./Hermes-2-Pro-Mistral-7B.Q8_0.gguf" TEMPLATE """{{ if .System }}<|im_start|>system {{ .System }}<|im_end|> {{ end }}{{ if .Prompt }}<|im_start|>user {{ .Prompt }}<|im_end|> {{ end }}<|im_start|>assistant """ PARAMETER stop <|im_start|> PARAMETER stop <|im_end|> PARAMETER num_ctx 4096 ``` 4. Create the model from the modelfile ```bash ollama create hermes2-pro-mistral:7b-q8_0 -f ./Modelfile ``` 5. Run `ollama list` and see if the model is registered from the modelfile (it is not). Rolled back to [Ollama v0.1.32](https://github.com/ollama/ollama/releases/tag/v0.1.32), and it is now working again so i'm pretty sure this is a problem with the version 0.1.33. ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.1.33

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PR_kwDOJ0Z1Ps5U8rWu

if directory cannot be resolved, do not fail

closed

CONTRIBUTOR

allow for offline mode

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I_kwDOJ0Z1Ps6AjghG

Why the Gemma performs that bad with some simple questions?

closed

NONE

Just tried `Gemma` model but not sure why it performed that bad. ```sh ollama pull gemma:2b ollama run gemma:2b ``` **So is it the model issue or the model hosted here is different?** For example - this should be a common-sense question but it has no idea: ``` >>> Do you think whether human can fly? Tell me why with step-by-step reasoning. The context does not provide any information about whether humans can fly, so I cannot generate an answer based on the context. ``` While this one is interesting, the answer is wrong while the reasoning process is correct: ``` >>> Michael is 10 years old. Rob is 2 years older than Amy, while Amy is 5 years younger than Michael. How old is Rob? Rob is 12 years old. We know that Amy is 5 years younger than Michael, so she is 5 - 10 = 5 years old. Since Rob is 2 years older than Amy, he is 5 + 2 = 7 years old. ``` 

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I_kwDOJ0Z1Ps6BQgVp

Error: could not connect to ollama app, is it running?

closed

NONE

I think I removed everything, and reinstalled ollama on Ubuntu:22.04. After a “fresh” install, the command line can not connect to ollama app. ``` Ubuntu： ~ $ curl -fsSL https://ollama.com/install.sh | sh >>> Downloading ollama... ######################################################################## 100.0%#=#=-# # ######################################################################## 100.0% >>> Installing ollama to /usr/local/bin... [sudo] password for user: >>> Adding ollama user to render group... >>> Adding ollama user to video group... >>> Adding current user to ollama group... >>> Creating ollama systemd service... >>> Enabling and starting ollama service... >>> NVIDIA GPU installed. Ubuntu： ~ $ ollama Usage: ollama [flags] ollama [command] Available Commands: serve Start ollama create Create a model from a Modelfile show Show information for a model run Run a model pull Pull a model from a registry push Push a model to a registry list List models cp Copy a model rm Remove a model help Help about any command Flags: -h, --help help for ollama -v, --version Show version information Use "ollama [command] --help" for more information about a command. Ubuntu： ~ $ ollama list Error: could not connect to ollama app, is it running? Ubuntu： ~ $ sudo service ollama status ● ollama.service - Ollama Service Loaded: loaded (/etc/systemd/system/ollama.service; enabled; vendor preset: enabled) Drop-In: /etc/systemd/system/ollama.service.d └─override.conf Active: active (running) since Tue 2024-03-05 16:04:03 CST; 28s ago Main PID: 605340 (ollama) Tasks: 27 (limit: 76514) Memory: 486.3M CPU: 7.895s CGroup: /system.slice/ollama.service └─605340 /usr/local/bin/ollama serve 3月 05 16:04:03 Ubuntu ollama[605340]: time=2024-03-05T16:04:03.849+08:00 level=INFO source=images.go:717 msg="total unused blobs removed: 0" 3月 05 16:04:03 Ubuntu ollama[605340]: time=2024-03-05T16:04:03.849+08:00 level=INFO source=routes.go:1019 msg="Listening on [::]:33020 (version 0.1.27)" 3月 05 16:04:03 Ubuntu ollama[605340]: time=2024-03-05T16:04:03.850+08:00 level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..." 3月 05 16:04:08 Ubuntu ollama[605340]: time=2024-03-05T16:04:08.164+08:00 level=INFO source=payload_common.go:146 msg="Dynamic LLM libraries [cpu_avx cpu > 3月 05 16:04:08 Ubuntu ollama[605340]: time=2024-03-05T16:04:08.164+08:00 level=INFO source=gpu.go:94 msg="Detecting GPU type" 3月 05 16:04:08 Ubuntu ollama[605340]: time=2024-03-05T16:04:08.164+08:00 level=INFO source=gpu.go:265 msg="Searching for GPU management library libnvidia> 3月 05 16:04:08 Ubuntu ollama[605340]: time=2024-03-05T16:04:08.168+08:00 level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [/usr/lib/x86_64-lin> 3月 05 16:04:08 Ubuntu ollama[605340]: time=2024-03-05T16:04:08.175+08:00 level=INFO source=gpu.go:99 msg="Nvidia GPU detected" 3月 05 16:04:08 Ubuntu ollama[605340]: time=2024-03-05T16:04:08.175+08:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" 3月 05 16:04:08 Ubuntu ollama[605340]: time=2024-03-05T16:04:08.191+08:00 level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 8.6" ``` ``` [Service] Environment="OLLAMA_HOST=0.0.0.0:33020" ``` I have to use ollama serve first then I can pull model files. If I check the service port, both 33020 and 11434 are in service. If the ollama is running as a service, do I suppose to download model file directly without launch another ollama serve from command line? Thanks

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I_kwDOJ0Z1Ps6H2r3k

Bunny-Llama-3-8B-V

open

NONE

Would love to see: [Bunny-Llama-3-8B-V](https://huggingface.co./BAAI/Bunny-Llama-3-8B-V) included in the Ollama models. > Bunny is a family of lightweight but powerful multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Llama-3-8B, Phi-1.5, StableLM-2, Qwen1.5, MiniCPM and Phi-2. To compensate for the decrease in model size, we construct more informative training data by curated selection from a broader data source.

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I_kwDOJ0Z1Ps6VItZl

n_ctx parameter display error

closed

NONE

When I create model from a Modelfile: ```log FROM mistral:latest PARAMETER num_ctx 4096 ``` and load with: ```log ollama create mistral:latest-nctx4096 -f Modelfile ollama run mistral:latest-nctx4096 ``` setting `n_ctx` to 4096, it is discovered that on the server end `n_ctx` is wrongly displayed as `16384`:  ### OS Linux ### GPU _No response_ ### CPU Intel ### Ollama version 0.3.9

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PR_kwDOJ0Z1Ps5ygAj8

openai: do not set temperature to 0 when setting seed

closed

MEMBER

`tempearture` was previously set to 0 for reproducible outputs when setting seed, however this is not required Note https://github.com/ollama/ollama/issues/4990 is still an open issue on Nvidia/AMD GPUs Fixes https://github.com/ollama/ollama/issues/5044

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I_kwDOJ0Z1Ps5z4ZBZ

Unable to download any models on Amazon Linux 2023 on EC2

closed

NONE

I am trying to download LLama2 and Medllama2 on Amazon Linux 2023. I have verified the security group to check if it has permissions to reach outside world and it does. Ollama installation was successful by using ``` curl https://ollama.ai/install.sh | sh ``` However, when I try to run `ollama pull medllama2` it errors out with the following. ``` Error: Head "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/7d/7da0afc1bbe70f988bc3c7f07d7dfcd16230d0214724a6dc1c639a56657a385f/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20231016%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20231016T010928Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=76b083752834ca2f16ba864c9aa146a90d0ec203696e415f6ff20eca3ea74f20": read tcp 10.220.8.84:38428->104.18.9.90:443: read: connection reset by peer ``` I have tried multiple models and everything fails. Here is Openssl version information. ``` OpenSSL 3.0.8 7 Feb 2023 (Library: OpenSSL 3.0.8 7 Feb 2023) ollama version 0.1.3 ```

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I_kwDOJ0Z1Ps6g8Lsc

Deepseek (various) 236b crashes on run

open

NONE