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@@ -10,17 +10,25 @@ datasets:
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  - augmxnt/ultra-orca-boros-en-ja-v1
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  ---
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- shisa-v2 Base Model ablation
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- This model uses a LR of 8e-6 that slightly improves performance vs the original 2e-5
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- It also uses NEFTune, although the expected impact may be neglible for this dataset.
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- (this appears to validate the Llama 3 8B LR ablations for predicting improved LR hyperparameter)
 
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- While the last model matched gpt-3.5-turbo, I think it's fair to say that this model allows us to farily say that it "beats" it.
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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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  |----------------------------------------|---------|-----------------|----------|--------|-------------|
@@ -29,6 +37,7 @@ Using a [fork](https://github.com/shisa-ai/shaberi) of [Lightblue's Shaberi benc
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  | **shisa-ai/shisa-v1-llama3-70b** | **7.30**| **7.34** | **7.67** | **8.15** | **6.04** |
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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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  - augmxnt/ultra-orca-boros-en-ja-v1
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  ---
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+ # shisa-v2 Base Model ablation
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+ This is a fine-tune Llama 3 70B Instruct with the primary `shisa-v1` dataset to improve Japanese language capabilities.
 
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+ This model uses a LR of 8e-6 that slightly improves performance vs the original 2e-5 tune (based on and validating predictive power of the the
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+ results of the Llama 3 8B LR ablations).
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+ It also uses NEFTune, although the expected impact is neglible for this dataset.
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+ While the 2e-5 model matched gpt-3.5-turbo performance, this 2e6 version consistently edges it out, so I think it's fair to say that this model "beats" it.
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+ There are a selection of GGUF quants here: https://huggingface.co/shisa-ai/shisa-v1-llama3-70b-gguf
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+
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+ While this is merely a test ablation on the road to `shisa-v2`, as the strongest commercially usable open JA model I've tested so far, this model may be of general interest.
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+
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+ ## Performance
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+
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+ Measured 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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  | **shisa-ai/shisa-v1-llama3-70b** | **7.30**| **7.34** | **7.67** | **8.15** | **6.04** |
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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-8x7b-chat-v0.1 | 7.00 | 7.18 | 6.30 | 7.98 | 6.55 |
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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** |