--- language: - en license: other library_name: transformers tags: - mergekit - merge - Yi license_name: yi-license license_link: https://huggingface.co./01-ai/Yi-34B/blob/main/LICENSE base_model: [] model-index: - name: Yi-34B-200K-DARE-merge-v7 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.09 name: normalized accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v7 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.99 name: normalized accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v7 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 77.3 name: accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v7 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 58.9 source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v7 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 83.11 name: accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v7 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 65.35 name: accuracy source: url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v7 name: Open LLM Leaderboard --- # Possibly made obsolete by: https://huggingface.co./brucethemoose/Yi-34B-200K-DARE-megamerge-v8 # Yi 34B 200K DARE Merge v7 A merge of several Yi 34B 200K models using the new DARE Ties method via mergekit. The goal is to create a merge model that excels at 32K+ context performance. ## Prompt template: Orca-Vicuna ``` SYSTEM: {system_message} USER: {prompt} ASSISTANT: ``` It might recognize ChatML, and possibly Alpaca-like formats. Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/ ## Running Being a Yi model, try running a lower temperature with 0.02-0.06 MinP, a little repetition penalty, maybe mirostat with a low tau, and no other samplers. Yi tends to run "hot" by default, and it really needs a low temperature + MinP to cull the huge vocabulary. 24GB GPUs can efficiently run Yi-34B-200K models at **45K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). 16GB GPUs can still run the high context with aggressive quantization. To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends like exllamav2 or unsloth. ## Testing Notes See: https://huggingface.co./brucethemoose/Yi-34B-200K-DARE-merge-v5#testing-notes A "4k" merge model was created to try and extend the context of SUS Chat and DPO-bagel before adding them to the merge: https://huggingface.co./brucethemoose/SUS-Bagel-200K-DARE-Test In addition, the weight gradients are biased towards Vicuna-format models in the first few layers to try and "emphasize" the Orca-Vicuna prompt template. How sucessful this is remains to be seen. ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base. ### Models Merged The following models were included in the merge: * https://huggingface.co./kyujinpy/PlatYi-34B-200k-Q-FastChat * https://huggingface.co./jondurbin/bagel-34b-v0.2 * https://huggingface.co./NousResearch/Nous-Capybara-34B * https://huggingface.co./migtissera/Tess-M-Creative-v1.0 * https://huggingface.co./brucethemoose/SUS-Bagel-200K-DARE-Test * https://huggingface.co./Mihaiii/Pallas-0.5 * https://huggingface.co./bhenrym14/airoboros-3_1-yi-34b-200k * https://huggingface.co./adamo1139/Yi-34B-200K-AEZAKMI-v2 * https://huggingface.co./migtissera/Tess-34B-v1.4 * https://huggingface.co./SUSTech/SUS-Chat-34B * https://huggingface.co./jondurbin/bagel-dpo-34b-v0.2 * https://huggingface.co./chargoddard/Yi-34B-200K-Llama * https://huggingface.co./chargoddard/Yi-34B-Llama ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama # No parameters necessary for base model - model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4 parameters: weight: [0.23, 0.125, 0.125, 0.125, 0.125, 0.125] density: 0.59 - model: /home/alpha/Models/Raw/Mihaiii_Pallas-0.5 parameters: weight: [0.23, 0.125, 0.125, 0.125, 0.125, 0.125] density: 0.59 - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k parameters: weight: [0.02, 0.106, 0.106, 0.106, 0.106, 0.106] density: 0.59 - model: /home/alpha/Storage/Models/Raw/jondurbin_bagel-34b-v0.2 #Only the SFT in the main merge since the DPO version seems to have no long context ability at all parameters: weight: [0.02, 0.100, 0.100, 0.100, 0.100, 0.100] density: 0.4 - model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat parameters: weight: [0.02, 0.100, 0.100, 0.100, 0.100, 0.100] density: 0.59 #- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k # Dolphin 200K seems to be funky according to multiple leaderboards and perplexity tests? # parameters: # weight: 0.15 # density: 0.6 - model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2 parameters: weight: [0.02, 0.110, 0.110, 0.110, 0.110, 0.110] density: 0.59 - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B parameters: weight: [0.22, 0.126, 0.126, 0.126, 0.126, 0.126] density: 0.59 - model: /home/alpha/Storage/Models/Raw/4kmerge parameters: weight: [0.02, 0.108, 0.108, 0.108, 0.108, 0.108] density: 0.5 - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 parameters: weight: [0.22, 0.100, 0.100, 0.100, 0.100, 0.10] density: 0.59 merge_method: dare_ties tokenizer_source: union base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama parameters: int8_mask: true dtype: bfloat16 ``` The following config was used for the "4kmerge" model: ```yaml models: - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama # No parameters necessary for base model - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama parameters: weight: 0.5 density: 1 - model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B parameters: weight: 0.2 density: 0.12 - model: /home/alpha/Models/Raw/jondurbin_bagel-dpo-34b-v0.2 parameters: weight: 0.2 density: 0.15 - model: /home/alpha/Models/Raw/jondurbin_bagel-34b-v0.2 parameters: weight: 0.1 density: 0.12 merge_method: dare_ties tokenizer_source: union base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama parameters: int8_mask: true dtype: bfloat16 ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_brucethemoose__Yi-34B-200K-DARE-merge-v7) | Metric |Value| |---------------------------------|----:| |Avg. |73.12| |AI2 Reasoning Challenge (25-Shot)|68.09| |HellaSwag (10-Shot) |85.99| |MMLU (5-Shot) |77.30| |TruthfulQA (0-shot) |58.90| |Winogrande (5-shot) |83.11| |GSM8k (5-shot) |65.35|