Create README.md

README.md

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+---
+license: other
+license_name: llama-3
+license_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/raw/main/LICENSE
+
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+tags:
+- generated_from_trainer
+model-index:
+- name: workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct
+  results: []
+---
+
+<p align="center">
+  <img width=400 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kg3QjQOde0X743csGJT-f.png" alt="Suzume - a Japanese tree sparrow"/>
+</p>
+
+# Suzume
+
+This Suzume 8B, a Japanese finetune of Llama 3.
+
+Llama 3 has exhibited excellent performance on many English language benchmarks. 
+However, it also seemingly been finetuned on mostly English data, meaning that it will respond in English, even if prompted in Japanese.
+
+We have fine-tuned Llama 3 on almost 3,000 Japanese conversations meaning that this model has the smarts of Llama 3 but has the added ability to chat in Japanese.
+
+Please feel free to comment on this model and give us feedback in the Community tab!
+
+# How to use
+
+You can use the original trained model with vLLM like so:
+
+```python
+from vllm import LLM, SamplingParams
+
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+llm = LLM(model="lightblue/suzume-llama-3-8B-japanese")
+
+prompts = [
+  "東京のおすすめの観光スポットを教えて下さい",
+]
+
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+# Evaluation scores
+
+We find that this is the best performing model in the 7/8B class of LLMs on a multitude of Japanese language benchmarks.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/2obyDbrjiNV3PGfwom6EI.png)
+
+# Training data
+
+We train on three sources of data to create this model
+
+* [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja) - 669 conversations
+    * A hand-edited dataset of nearly 700 conversations taken originally from translations of the [kunishou/hh-rlhf-49k-ja](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) dataset.
+* [openchat/openchat_sharegpt4_dataset](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json) (Japanese conversations only) - 167 conversations
+    * Conversations taken from humans talking to GPT-4
+* lightblue/tagengo-gpt4 (Japanese prompts only) (Link coming soon!) - 2,482 conversations
+    * Almost 2,500 diverse Japanese prompts sampled from [lmsys/lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) and then used to prompt `gpt-4-0125-preview`
+
+# Training config
+
+[<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)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.0`
+```yaml
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: /workspace/llm_training/axolotl/llama3-ja/openchat_megagon_lbgpt4_ja.json
+    ds_type: json # see other options below
+    type: sharegpt
+    conversation: llama-3
+dataset_prepared_path: /workspace/llm_training/axolotl/llama3-ja/prepared_openchat_megagon_lbgpt4_ja
+val_set_size: 0.01
+output_dir: /workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct
+
+sequence_len: 8192
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: False
+
+use_wandb: true
+wandb_project: axolotl
+wandb_entity: peterd
+wandb_name: openchat_megagon_lbgpt4_ja_8B_instruct
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 5
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
+weight_decay: 0.0
+special_tokens:
+  pad_token: <|end_of_text|>
+```
+
+</details><br>
+
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 3
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 12
+- total_eval_batch_size: 6
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 1.303         | 0.08  | 1    | 1.2664          |
+| 1.4231        | 0.23  | 3    | 1.2409          |
+| 1.1007        | 0.46  | 6    | 1.0264          |
+| 1.0635        | 0.69  | 9    | 1.0154          |
+| 1.0221        | 0.92  | 12   | 0.9555          |
+
+
+### Framework versions
+
+- Transformers 4.40.0.dev0
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.0