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--- |
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license: other |
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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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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- text-generation-inference |
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--- |
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**Dolphin-2.2-yi-34b-200k**, **Nous-Capybara-34B**, **Tess-M-v1.3**, **Airoboros-3_1-yi-34b-200k**, **PlatYi-34B-Q**, and **Una-xaberius-34b-v1beta** 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](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-34B-v1.4 |
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parameters: |
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weight: 0.19 |
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density: 0.44 |
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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: 0.14 |
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density: 0.34 |
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- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B |
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parameters: |
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weight: 0.19 |
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density: 0.44 |
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- model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200K-Q |
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parameters: |
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weight: 0.14 |
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density: 0.34 |
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- model: /home/alpha/FastModels/ehartford_dolphin-2.2-yi-34b-200k |
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parameters: |
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weight: 0.19 |
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density: 0.44 |
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- model: /home/alpha/FastModels/fblgit_una-xaberius-34b-v1beta |
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parameters: |
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weight: 0.15 |
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density: 0.08 |
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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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## Testing |
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Various densities were tested with perplexity tests and high context test prompts. Relatively high densities seem to perform better, contrary to the findings of the Super Mario paper. |
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Weights that add up to 1 seems to be optimal. |
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Dare Ties is also resulting in better merges than regular Ties merge (which was already excellent) |
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Xaberuis is not a 200K model, hence it was merged at a very low density to try and preserve Yi 200K's long context performance while still inheriting some of Xaberius's performance. |
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I chose not to include other finetunes 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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It might recognize ChatML from Dolphin+Xaberius, and Llama-chat from Airoboros. |
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Being a Yi model, try disabling the BOS token and/or running a lower temperature with 0.05-0.13 MinP, a little repitition penalty, and no other samplers. 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. |
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To load this in full-context backends like transformers and vllm, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! |
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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/) |
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I recommend exl2 quantizations profiled on data similar to the desired task. It is especially sensitive to the quantization data at low bpw! |
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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/ehartford/dolphin-2.2-yi-34b-200k |
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https://huggingface.co/kyujinpy/PlatYi-34B-Q |
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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/fblgit/una-xaberius-34b-v1beta |
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https://huggingface.co/chargoddard/Yi-34B-200K-Llama |
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https://huggingface.co/01-ai/Yi-34B-200K |
