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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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- mergekit |
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- merge |
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- Yi |
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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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base_model: [] |
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model-index: |
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- name: Yi-34B-200K-DARE-megamerge-v8 |
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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: 67.75 |
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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/Yi-34B-200K-DARE-megamerge-v8 |
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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: 86.06 |
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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/Yi-34B-200K-DARE-megamerge-v8 |
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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: 77.03 |
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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/Yi-34B-200K-DARE-megamerge-v8 |
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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: 56.31 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-megamerge-v8 |
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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: 82.79 |
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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/Yi-34B-200K-DARE-megamerge-v8 |
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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: 65.43 |
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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/Yi-34B-200K-DARE-megamerge-v8 |
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name: Open LLM Leaderboard |
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--- |
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# Yi 34B 200K DARE Merge v8 |
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A merge of many 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, without any additional finetuning. |
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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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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/ |
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## Running |
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Being a Yi model, run a lower temperature with 0.1 or higher 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 Yi's huge vocabulary. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841 |
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24GB GPUs can efficiently run Yi-34B-200K models at **40K-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. |
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Lonestriker has also uploaded general purpose quantizations here: https://huggingface.co/models?sort=trending&search=LoneStriker+Yi-34B-200K-DARE-megamerge-v8 |
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Additionally, TheBloke has uploaded experimental GGUFs using llama.cpp's new imatrix quantization feature, profiled on VMware open-instruct: https://huggingface.co/TheBloke/Yi-34B-200K-DARE-megamerge-v8-GGUF |
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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, litellm or unsloth. |
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## Testing Notes |
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See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5#testing-notes |
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An intermediate merge model was created to try and extend the context of several 4k models before adding them to the main merge, as seen in the "megamerge" recipe below. I can upload this upon request |
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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. |
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## Merge Details |
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### Merge Method |
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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. |
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### Models Merged |
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The following models were included in the merge: |
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* https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat |
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* https://huggingface.co/jondurbin/bagel-34b-v0.2 |
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* https://huggingface.co/migtissera/Tess-M-Creative-v1.0 |
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* https://huggingface.co/brucethemoose/SUS-Bagel-200K-DARE-Test |
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* https://huggingface.co/Mihaiii/Pallas-0.5 |
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* https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k |
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* https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2 |
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* https://huggingface.co/migtissera/Tess-34B-v1.4 |
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* https://huggingface.co/SUSTech/SUS-Chat-34B |
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* https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2 |
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* https://huggingface.co/bhenrym14/platypus-yi-34b |
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* https://huggingface.co/Weyaxi/Nous-Hermes-2-SUS-Chat-34B-Slerp |
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* https://huggingface.co/TriadParty/deepsex-34b |
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* https://huggingface.co/TriadParty/deepmoney-34b-200k-base |
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* https://huggingface.co/chargoddard/Yi-34B-200K-Llama |
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* https://huggingface.co/chargoddard/Yi-34B-Llama |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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models: |
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- model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama |
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# No parameters necessary for base model |
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- model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama |
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#200K base to extend the context of 4K models, max density as we *want* it to 'interfere' |
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parameters: |
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weight: 0.33 |
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density: 1 |
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- model: /home/alpha/Models/Raw/Weyaxi_Nous-Hermes-2-SUS-Chat-34B-Slerp |
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parameters: |
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weight: 0.15 |
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density: 0.36 |
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- model: /home/alpha/Models/Raw/jondurbin_bagel-dpo-34b-v0.2 |
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#Mix dpo with sft to tone down dpo |
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parameters: |
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weight: 0.06 |
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density: 0.36 |
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- model: /home/alpha/Models/Raw/jondurbin_bagel-34b-v0.2 |
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parameters: |
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weight: 0.06 |
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density: 0.41 |
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- model: /home/alpha/Models/Raw/bhenrym14_platypus-yi-34b |
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#Vicuna format |
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parameters: |
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weight: 0.19 |
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density: 0.41 |
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# - model: /home/alpha/Models/Raw/01-ai_Yi-34B-Chat #+/home/alpha/Models/Raw/Doctor-Shotgun_limarpv3-yi-llama-34b-lora |
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# #Can't get lora OR base model to work without erroring out? |
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# parameters: |
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# weight: 0.04 |
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# density: 0.36 |
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- model: /home/alpha/Models/Raw/TriadParty_deepsex-34b |
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#Base model with no prompt |
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parameters: |
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weight: 0.21 |
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density: 0.39 |
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merge_method: dare_ties |
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tokenizer_source: union |
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base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-Llama |
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parameters: |
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int8_mask: true |
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dtype: bfloat16 |
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name: 4kmerge-v2 |
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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-34B-v1.4 |
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#Emphasize the beginning of Vicuna format models |
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parameters: |
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weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113] |
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density: 0.61 |
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- model: /home/alpha/Models/Raw/Mihaiii_Pallas-0.5 |
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# Vicuna format |
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parameters: |
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weight: [0.22, 0.113, 0.113, 0.113, 0.113, 0.113] |
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density: 0.61 |
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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: [0.02, 0.081, 0.081, 0.081, 0.081, 0.081] |
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density: 0.59 |
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- model: /home/alpha/Storage/Models/Raw/jondurbin_bagel-34b-v0.2 |
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#Only the SFT in the main merge since the DPO version seems to have no long context ability at all, and some overfitting(?) issues |
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parameters: |
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weight: [0.02, 0.093, 0.093, 0.093, 0.093, 0.093] |
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density: 0.4 |
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- model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat |
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parameters: |
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weight: [0.02, 0.081, 0.081, 0.081, 0.081, 0.081] |
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density: 0.59 |
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#- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k |
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# Dolphin 200K seems to be funky according to multiple leaderboards and perplexity tests? |
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# parameters: |
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# weight: 0.15 |
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# density: 0.6 |
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- model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2 |
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parameters: |
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weight: [0.02, 0.096, 0.096, 0.096, 0.096, 0.096] |
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density: 0.59 |
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- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B |
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parameters: |
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weight: [0.21, 0.115, 0.115, 0.115, 0.115, 0.115] |
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density: 0.59 |
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- model: 4kmerge-v2 |
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#Previous merge |
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parameters: |
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weight: [0.02, 0.115, 0.115, 0.115, 0.115, 0.115] |
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density: 0.4 |
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- model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 |
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# Vicuna format |
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parameters: |
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weight: [0.21, 0.09, 0.09, 0.09, 0.09, 0.09] |
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density: 0.61 |
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- model: /home/alpha/Models/Raw/TriadParty_deepmoney-34b-200k-base |
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# No prompt format, native long context full finetune |
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parameters: |
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weight: [0.04, 0.103, 0.103, 0.103, 0.103, 0.103] |
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density: 0.61 |
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merge_method: dare_ties |
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tokenizer_source: union |
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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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## Self Promotion |
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I'm part of a AI startup called Holocene AI! |
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We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups. |
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Contact me at: [email protected] |
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I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: https://ko-fi.com/alphaatlas |
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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__Yi-34B-200K-DARE-megamerge-v8) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |72.56| |
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|AI2 Reasoning Challenge (25-Shot)|67.75| |
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|HellaSwag (10-Shot) |86.06| |
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|MMLU (5-Shot) |77.03| |
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|TruthfulQA (0-shot) |56.31| |
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|Winogrande (5-shot) |82.79| |
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|GSM8k (5-shot) |65.43| |
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