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--- |
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language: |
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- en |
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license: other |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- merge |
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license_name: yi-license |
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license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE |
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pipeline_tag: text-generation |
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model-index: |
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- name: CapyTessBorosYi-34B-200K-DARE-Ties |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 64.93 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 85.92 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 76.18 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 55.84 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 83.03 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 61.94 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties |
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name: Open LLM Leaderboard |
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--- |
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# Obsolete, see: https://huggingface.co/brucethemoose/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-HighDensity |
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*** |
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**NousResearch/Nous-Capybara-34B**, **migtissera/Tess-M-v1.3** and **bhenrym14/airoboros-3_1-yi-34b-200k** merged with a new, experimental implementation of "dare ties" via mergekit. See: |
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> Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch |
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https://github.com/yule-BUAA/MergeLM |
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https://github.com/cg123/mergekit/tree/dare' |
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Merged with the following config, and the tokenizer from chargoddard's Yi-Llama: |
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``` |
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models: |
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- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama |
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# no parameters necessary for base model |
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- model: /home/alpha/Storage/Models/Raw/migtissera_Tess-M-v1.3 |
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parameters: |
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weight: 0.41 |
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density: 0.50 |
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- model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k |
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parameters: |
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weight: 0.18 |
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density: 0.46 |
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- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B |
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parameters: |
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weight: 0.41 |
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density: 0.50 |
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merge_method: dare_ties |
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base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama |
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parameters: |
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int8_mask: true |
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dtype: bfloat16 |
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``` |
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dare_ties is testing with better perplexity than a regular ties merge with the same merge configuration. Model weights that add up to one also seem optimal from testing. And high context results seem... better than the previous dare merge with Tess 1.2. |
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I chose not to include other finetunes, such as Dolphin, because they aren't trained on the 200K base. If any other 200K finetunes pop up, let me know. |
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*** |
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## Prompt template: Orca-Vicuna |
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``` |
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SYSTEM: {system_message} |
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USER: {prompt} |
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ASSISTANT: |
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``` |
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Being a Yi model, try disabling the BOS token and/or running a lower temperature with MinP (and no other samplers) if output doesn't seem right. Yi tends to run "hot" by default. |
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Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition. It also might respond to the llama-2 chat format. |
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*** |
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24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2. 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/), and recommend exl2 quantizations on data similar to the desired task, such as these targeted at story writing: [4.0bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-4bpw-fiction) / [3.1bpw](https://huggingface.co/brucethemoose/CapyTessBorosYi-34B-200K-DARE-Ties-exl2-3.1bpw-fiction) |
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*** |
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Credits: |
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https://github.com/cg123/mergekit/tree/dare |
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https://huggingface.co/NousResearch/Nous-Capybara-34B/ |
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https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k |
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https://huggingface.co/migtissera/Tess-M-v1.3 |
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https://huggingface.co/chargoddard/Yi-34B-200K-Llama |
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https://huggingface.co/01-ai/Yi-34B-200K |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_brucethemoose__CapyTessBorosYi-34B-200K-DARE-Ties) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |71.31| |
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|AI2 Reasoning Challenge (25-Shot)|64.93| |
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|HellaSwag (10-Shot) |85.92| |
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|MMLU (5-Shot) |76.18| |
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|TruthfulQA (0-shot) |55.84| |
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|Winogrande (5-shot) |83.03| |
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|GSM8k (5-shot) |61.94| |
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