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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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- merge |
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--- |
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### Obsolete, see https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5 |
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*** |
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**Dolphin-2.2-yi-34b-200k**, **Nous-Capybara-34B**, **Tess-M-v1.4**, **Airoboros-3_1-yi-34b-200k**, **PlatYi-34B-200K-Q**, and **Una-xaberius-34b-v1beta** merged with a new, experimental implementation of "dare ties" via mergekit. |
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Quantized with the git version of exllamav2 with 200 rows (400K tokens) on a long Orca-Vicuna format chat, a selected sci fi story and a fantasy story. This should hopefully yield better chat/storytelling performance than the short, default wikitext quantization. |
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4bpw is enough for **~47K context on a 24GB GPU.** I would highly recommend running in exui for speed at long context. I go into more detail in this [Reddit post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/) |
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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: 0.6 |
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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: 0.5 |
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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: 0.6 |
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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: 0.5 |
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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: 0.6 |
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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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First exllama quantization pass: |
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``` |
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python convert.py --in_dir //home/alpha/FastModels/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/mes.json --cal_dataset /home/alpha/Documents/smol.parquet -l 2048 -r 80 -ml 2048 -mr 40 -gr 40 -ss 4096 -nr -b 4.0 -hb 6 |
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``` |
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Second exllama quantization pass: |
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``` |
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python convert.py --in_dir /home/alpha/FastModels/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/mes.json --cal_dataset /home/alpha/Documents/medium.parquet -l 2048 -r 200 -ml 2048 -mr 40 -gr 200 -ss 4096 -b 4.0 -hb 6 -cf /home/alpha/FastModels/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-exl2-4bpw-fiction -nr |
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``` |
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## Testing Notes |
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Various densities were tested with perplexity tests and high context 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 seemingly better, lower perplexity merges than a regular ties merge, task arithmetic or a slerp merge. |
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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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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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*** |
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## Running |
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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 repetition penalty, and no other samplers. Yi tends to run "hot" by default. |
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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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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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## Credits: |
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https://github.com/turboderp/exllamav2 |
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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-200K-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-v1.4 |
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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 |
