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--- |
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license: llama3 |
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datasets: |
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- augmxnt/ultra-orca-boros-en-ja-v1 |
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language: |
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- ja |
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- en |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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--- |
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shisa-v2 Base Model ablation |
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Using a [fork](https://github.com/shisa-ai/shaberi) of [Lightblue's Shaberi benchmark framework](https://github.com/lightblue-tech/japanese_llm_eval): |
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| Model | Average | ELYZA-tasks-100 | MT-Bench | Rakuda | Tengu-Bench | |
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|----------------------------------------|---------|-----------------|----------|--------|-------------| |
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| gpt-4-turbo-2024-04-09 | 8.75 | 8.78 | 8.74 | 9.18 | 8.31 | |
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| CohereForAI/c4ai-command-r-plus | 7.69 | 7.50 | 7.43 | 9.05 | 6.79 | |
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| gpt-3.5-turbo-0125 | 7.17 | 7.24 | 6.98 | 7.64 | 6.82 | |
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| **shisa-ai/shisa-v1-llama3-70b** | **7.17**| **7.16** | **7.45** | **7.98** | **6.09** | |
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| karakuri-ai/karakuri-lm-70b-chat-v0.1 | 6.84 | 6.86 | 6.43 | 7.85 | 6.23 | |
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| lightblue/ao-karasu-72B | 6.81 | 7.19 | 6.54 | 7.25 | 6.27 | |
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| **shisa-ai/shisa-v1-llama3-8b^** | **6.29**| **6.62** | **6.41** | **7.05**|**5.07** | |
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| shisa-ai/shisa-swallowmx-13a47b-v1 | 6.17 | 6.48 | 6.07 | 7.11 | 5.03 | |
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| **shisa-ai/shisa-v1-llama3-8b** | **6.10**| **6.52** | **6.20** | **6.37**|**5.33** | |
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| Rakuten/RakutenAI-7B-chat | 5.58 | 5.92 | 4.60 | 6.58 | 5.24 | |
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| shisa-ai/shisa-v1-gemma-8b | 5.64 | 6.50 | 5.42 | 5.10 | 5.55 | |
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| augmxnt/shisa-gamma-7b-v1 | 5.56 | 5.84 | 4.00 | 6.73 | 5.68 | |
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| lightblue/qarasu-14B-chat-plus-unleashed | 5.20 | 5.58 | 4.74 | 5.46 | 5.01 | |
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| cyberagent/calm2-7b-chat | 4.76 | 4.90 | 3.58 | 5.75 | 4.81 | |
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| mistralai/Mistral-7B-Instruct-v0.2 | 4.69 | 5.78 | 4.65 | 3.80 | 4.53 | |
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| **shisa-ai/shisa-v1-yi1.5-9b** | **4.63**| **5.98** | **4.28** | **3.26**|**5.00** | |
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^ Shaberi uses `temperature=0.0`, no sampling, for all generations by default. This is actually different from [JA MT-Bench's default settings](https://github.com/Stability-AI/FastChat/blob/jp-stable/fastchat/llm_judge/common.py#L37) which has different temperature per category. |
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This means that Shaberi's results can't be compared to other JA MT-Bench results (like [my comparison chart](https://github.com/AUGMXNT/shisa/wiki/Evals-:-JA-MT%E2%80%90Bench) or the [Nejumi Leaderboard](https://wandb.ai/wandb-japan/llm-leaderboard/reports/Nejumi-LLM-Leaderboard-Evaluating-Japanese-Language-Proficiency--Vmlldzo2MzU3NzIy)). |
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Like some other models, if you look at the results you'll notice repetition loops. For Llama models, you usually want something like a `repetition_penalty` of 1.15/1.18 to get rid of repetition loops. |
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Because Shaberi uses the vLLM's OpenAI API server, it doesn't support repetition penalty, doing a `frequency_penalty` sweep (0.0, 0.5, 0.8) I found 0.5 to remove repetitions and improve output in general. There is no decay/window so for long generations, this may not be optimal. |
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For the improved generations, I used the following sampler settings: `temperature 0.2, min_p 0.1, frequency_penalty 0.5` (OpenAI doesn't support min_p, but vLLM adds it and it's [basically always the superior sampler](https://github.com/huggingface/transformers/issues/27670)). |
