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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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[**Nous-Capybara-34B**](https://huggingface.co/NousResearch/Nous-Capybara-34B/), [**Tess-M-v1.4**](https://huggingface.co/migtissera/Tess-34B-v1.4), [**Airoboros-3_1-yi-34b-200k**](https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k), [**PlatYi-34B-200K-Q**](https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat), [**Pallas-0.4**](https://huggingface.co/Mihaiii/Pallas-0.4), [**Yi-34B-200K-AEZAKMI-v2**](https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2), and a tiny bit of [**SUS-Chat-34B**](https://huggingface.co/SUSTech/SUS-Chat-34B) merged with a new, experimental implementation of "dare ties" via mergekit. |
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See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5 |
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The merge was then quantized with exllamav2's 0.0.11 brand new exl2 quantization, using 300K tokens from a sci fi story, a fantasy story, and a Vicuna format chat as profiling data, at a high context size. This should results in excellent writing performance for the model size. |
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This 4bpw quantization can fit ~**45K Context on a 24GB GPU** at high quality. |
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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, or maybe 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 running a lower temperature with 0.05-0.1 MinP, a little repitition 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, 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/) |
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*** |
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## Commands |
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First pass: |
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``` |
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python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/smol.parquet -ml 32768 -mr 9 -ss 4096 -b 4.0 -hb 6 -nr |
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``` |
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Second pass: |
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``` |
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python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/medium.parquet -l 12288 -r 29 -ml 32768 -mr 9 -ss 4096 -b 4.0 -hb 6 -cf /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5-exl2-4bpw-fiction -nr |
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``` |
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Merged in mergekit 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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# Less weight than previous merge since Pallas is a finetune of Tess |
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parameters: |
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weight: 0.14 |
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density: 0.62 |
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- model: /home/alpha/FastModels/Mihaiii_Pallas-0.4 |
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parameters: |
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weight: 0.14 |
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density: 0.62 |
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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: 0.52 |
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- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B |
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parameters: |
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weight: 0.22 |
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density: 0.62 |
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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: 0.14 |
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density: 0.52 |
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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 broken 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: 0.14 |
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density: 0.52 |
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- model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B/ |
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# Very low density and low weight since its a Yi 4K finetune, to try and preserve long context performance while "keeping" some of SUS |
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parameters: |
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weight: 0.08 |
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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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*** |
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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-v1.4 |
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https://huggingface.co/kyujinpy/PlatYi-34B-200k-Q-FastChat |
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https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2 |
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https://huggingface.co/Mihaiii/Pallas-0.4 |
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https://huggingface.co/SUSTech/SUS-Chat-34B |
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https://huggingface.co/chargoddard/Yi-34B-200K-Llama |
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https://huggingface.co/01-ai/Yi-34B-200K |