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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- Safetensors |
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- conversational |
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- text-generation-inference |
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- abliterated |
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- uncensored |
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- llama-cpp |
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- gguf-my-repo |
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base_model: huihui-ai/SmolLM2-1.7B-Instruct-abliterated |
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--- |
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# Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF |
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This model was converted to GGUF format from [`huihui-ai/SmolLM2-1.7B-Instruct-abliterated`](https://huggingface.co./huihui-ai/SmolLM2-1.7B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co./spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co./huihui-ai/SmolLM2-1.7B-Instruct-abliterated) for more details on the model. |
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--- |
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Model details: |
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- |
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This is an uncensored version of HuggingFaceTB/SmolLM2-1.7B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). |
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If the desired result is not achieved, you can clear the conversation and try again. |
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How to use |
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Transformers |
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pip install transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "huihui-ai/SmolLM2-1.7B-Instruct-abliterated" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` |
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) |
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messages = [{"role": "user", "content": "What is the capital of France."}] |
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input_text=tokenizer.apply_chat_template(messages, tokenize=False) |
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) |
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print(tokenizer.decode(outputs[0])) |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/SmolLM2-1.7B-Instruct-abliterated-Q6_K-GGUF --hf-file smollm2-1.7b-instruct-abliterated-q6_k.gguf -c 2048 |
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``` |
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