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--- |
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license: llama3 |
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base_model: meta-llama/Meta-Llama-3-70B-Instruct |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: outputs/basemodel-llama3-70b.8e6 |
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results: [] |
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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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*Per the Llama 3 Community License Agreement, the official name of this model is "Llama 3 shisa-v1-llama3-70b"* |
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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 initial 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 2e-6 version consistently edges it out, so I think it's fair to say that this model "beats" it. |
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While this is merely a test ablation on the road to `shisa-v2`, as of its release (mid-May 2024), it's the strongest commercially-usable open JA model benchmarked so far, so this model may be of general interest. |
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## Performance |
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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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| gpt-4-turbo-2024-04-09 | 8.75 | 8.78 | 8.74 | 9.18 | 8.31 | |
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| gpt-4o-2024-05-13 | 8.72 | 8.88 | 8.69 | 9.15 | 8.16 | |
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| gemini-1.5-pro | 8.58 | 8.58 | 8.93 | 9.20 | 7.61 | |
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| claude-3-opus-20240229 | 8.55 | 8.64 | 8.58 | 8.75 | 8.23 | |
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| CohereForAI/c4ai-command-r-plus | 7.69 | 7.50 | 7.43 | 9.05 | 6.79 | |
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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.2e5** | **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** | **6.59**| **6.67** | **6.95** | **7.05**| **5.68** | |
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| microsoft/Phi-3-medium-128k-instruct | 6.48 | 7.10 | 5.92 | 6.84 | 6.04 | |
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| **shisa-ai/shisa-v1-swallowmx-13a47b** | **6.17**| **6.48** | **6.07** | **7.11**| **5.03** | |
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| lightblue/suzume-llama-3-8B-japanese | 5.96 | 6.68 | 4.96 | 6.68 | 5.53 | |
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| augmxnt/shisa-gamma-7b-v1 | 5.82 | 5.96 | 5.02 | 6.85 | 5.47 | |
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| **shisa-ai/shisa-v1-phi3-14b** | **5.77**| **6.28** | **5.26** | **6.55**| **5.01** | |
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| **shisa-ai/shisa-v1-gemma-8b** | **5.64**| **6.50** | **5.42** | **5.10**| **5.55** | |
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| Rakuten/RakutenAI-7B-chat | 5.58 | 5.92 | 4.60 | 6.58 | 5.24 | |
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| lightblue/qarasu-14B-chat-plus-unleashed | 5.20 | 5.58 | 4.74 | 5.46 | 5.01 | |
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| **shisa-ai/shisa-v1-mistral0.3-7b** | **5.11**| **5.64** | **6.10** | **3.83**|**4.86** | |
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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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| augmxnt/shisa-7b-v1 | 4.50 | 4.63 | 3.95 | 4.89 | 4.53 | |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: meta-llama/Meta-Llama-3-70B-Instruct |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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# doesn't work... |
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# hub_model_id: shisa-ai/shisa-llama3-70b-v1 |
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# hub_strategy: end |
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use_wandb: true |
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wandb_project: shisa-v2 |
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wandb_entity: augmxnt |
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wandb_name: shisa-llama3-70b-v1.8e6 |
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chat_template: llama3 |
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datasets: |
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- path: augmxnt/ultra-orca-boros-en-ja-v1 |
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type: sharegpt |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.05 |
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output_dir: ./outputs/basemodel-llama3-70b.8e6 |
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sequence_len: 4096 |
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sample_packing: true |
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pad_to_sequence_len: true |
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neftune_noise_alpha: 5 |
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gradient_accumulation_steps: 2 |
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micro_batch_size: 2 |
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num_epochs: 3 |
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optimizer: paged_adamw_8bit |
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lr_scheduler: linear |
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learning_rate: 8e-6 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: true |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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early_stopping_patience: |
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resume_from_checkpoint: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_ratio: 0.1 |
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evals_per_epoch: 2 |
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eval_table_size: |
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saves_per_epoch: 0 |
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debug: |
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deepspeed: axolotl/deepspeed_configs/zero3_bf16.json |
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weight_decay: 0.05 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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# outputs/basemodel-llama3-70b.8e6 |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4440 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 8e-6 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 16 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 87 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.248 | 0.0033 | 1 | 0.7102 | |
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| 0.7497 | 0.5008 | 154 | 0.4374 | |
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| 0.7229 | 1.0016 | 308 | 0.3940 | |
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| 0.3772 | 1.4862 | 462 | 0.3962 | |
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| 0.3791 | 1.9870 | 616 | 0.3838 | |
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| 0.0943 | 2.4699 | 770 | 0.4440 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
