richarderkhov/lightblue_-_suzume-llama-3-8b-japanese-gguf overview

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    "summary": "[Paper] [Dataset] 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 more than 3,000 Japanese conversations meaning that this model has the intelligence 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! We will release a paper in the future describing how we made the training data, the model, and the evaluations we have conducted of it. # 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. We calculate our Japanese evaluation scores using our lightblue-tech/japanese_llm_eval repo. !image/png We also compare our Japanese model to our multilingual model using our multilingual_mt_bench repo. |                 | **lightblue/suzume-llama-3-8B-japanese** | **lightblue/suzume-llama-3-8B-multilingual** | **Nexusflow/Starling-LM-7B-beta** | **gpt-3.5-turbo** | |-----------------|------------------------------------------|----------------------------------------------|-----------------------------------|-------------------| | **Japanese 🇯🇵** | 6.24                                     | 6.56                                         | 6.22                              | 7.84              | Here, we find that our multilingual model outperforms our Japanese model on the Japanese MT-Bench benchmark, indicating that our multilingual model was able to generalize better to the Japanese MT-Bench benchmark from training on more data, even if that added data was not in Japanese. Note - the discrepancy between the MT-Bench scores of the first and second evaluation of lightblue/suzume-llama-3-8B-japanese are due to the difference in system message of the two evaluation harnesses. The former's system message is in Japanese while the latter's is in English. # Training data We train on three sources of data to create this model * megagonlabs/instruction_ja - 669 conversations * A hand-edited dataset of nearly 700 conversations taken originally from translations of the kunishou/hh-rlhf-49k-ja dataset. * openchat/openchat_sharegpt4_dataset (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 and then used to prompt gpt-4-0125-preview # Training config  See axolotl config 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: 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:  ``",
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    "readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nsuzume-llama-3-8B-japanese - GGUF\n- Model creator: https://huggingface.co/lightblue/\n- Original model: https://huggingface.co/lightblue/suzume-llama-3-8B-japanese/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [suzume-llama-3-8B-japanese.Q2_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q2_K.gguf) | Q2_K | 2.96GB |\n| [suzume-llama-3-8B-japanese.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.IQ3_XS.gguf) | IQ3_XS | 3.28GB |\n| [suzume-llama-3-8B-japanese.IQ3_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.IQ3_S.gguf) | IQ3_S | 3.43GB |\n| [suzume-llama-3-8B-japanese.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q3_K_S.gguf) | Q3_K_S | 3.41GB |\n| [suzume-llama-3-8B-japanese.IQ3_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.IQ3_M.gguf) | IQ3_M | 3.52GB |\n| [suzume-llama-3-8B-japanese.Q3_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q3_K.gguf) | Q3_K | 3.74GB |\n| [suzume-llama-3-8B-japanese.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q3_K_M.gguf) | Q3_K_M | 3.74GB |\n| [suzume-llama-3-8B-japanese.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q3_K_L.gguf) | Q3_K_L | 4.03GB |\n| [suzume-llama-3-8B-japanese.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.IQ4_XS.gguf) | IQ4_XS | 4.18GB |\n| [suzume-llama-3-8B-japanese.Q4_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q4_0.gguf) | Q4_0 | 4.34GB |\n| [suzume-llama-3-8B-japanese.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.IQ4_NL.gguf) | IQ4_NL | 4.38GB |\n| [suzume-llama-3-8B-japanese.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q4_K_S.gguf) | Q4_K_S | 4.37GB |\n| [suzume-llama-3-8B-japanese.Q4_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q4_K.gguf) | Q4_K | 4.58GB |\n| [suzume-llama-3-8B-japanese.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q4_K_M.gguf) | Q4_K_M | 4.58GB |\n| [suzume-llama-3-8B-japanese.Q4_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q4_1.gguf) | Q4_1 | 4.78GB |\n| [suzume-llama-3-8B-japanese.Q5_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q5_0.gguf) | Q5_0 | 5.21GB |\n| [suzume-llama-3-8B-japanese.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q5_K_S.gguf) | Q5_K_S | 5.21GB |\n| [suzume-llama-3-8B-japanese.Q5_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q5_K.gguf) | Q5_K | 5.34GB |\n| [suzume-llama-3-8B-japanese.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q5_K_M.gguf) | Q5_K_M | 5.34GB |\n| [suzume-llama-3-8B-japanese.Q5_1.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q5_1.gguf) | Q5_1 | 5.65GB |\n| [suzume-llama-3-8B-japanese.Q6_K.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q6_K.gguf) | Q6_K | 6.14GB |\n| [suzume-llama-3-8B-japanese.Q8_0.gguf](https://huggingface.co/RichardErkhov/lightblue_-_suzume-llama-3-8B-japanese-gguf/blob/main/suzume-llama-3-8B-japanese.Q8_0.gguf) | Q8_0 | 7.95GB |\n\n\n\n\nOriginal model description:\n---\nlicense: other\nlicense_name: llama-3\nlicense_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/raw/main/LICENSE\n\nbase_model: meta-llama/Meta-Llama-3-8B-Instruct\ntags:\n- generated_from_trainer\nmodel-index:\n- name: workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct\n  results: []\n---\n\n<p align=\"center\">\n  <img width=400 src=\"https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kg3QjQOde0X743csGJT-f.png\" alt=\"Suzume - a Japanese tree sparrow\"/>\n</p>\n\n# Suzume\n\n[[Paper](https://arxiv.org/abs/2405.12612)] [[Dataset](https://huggingface.co/datasets/lightblue/tagengo-gpt4)]\n\nThis Suzume 8B, a Japanese finetune of Llama 3.\n\nLlama 3 has exhibited excellent performance on many English language benchmarks. \nHowever, it also seemingly been finetuned on mostly English data, meaning that it will respond in English, even if prompted in Japanese.\n\nWe have fine-tuned Llama 3 on more than 3,000 Japanese conversations meaning that this model has the intelligence of Llama 3 but has the added ability to chat in Japanese.\n\nPlease feel free to comment on this model and give us feedback in the Community tab!\n\nWe will release a paper in the future describing how we made the training data, the model, and the evaluations we have conducted of it.\n\n# How to use\n\nYou can use the original trained model with vLLM like so:\n\n```python\nfrom vllm import LLM, SamplingParams\n\nsampling_params = SamplingParams(temperature=0.8, top_p=0.95)\n\nllm = LLM(model=\"lightblue/suzume-llama-3-8B-japanese\")\n\nprompts = [\n  \"東京のおすすめの観光スポットを教えて下さい\",\n]\n\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    prompt = output.prompt\n    generated_text = output.outputs[0].text\n    print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\n```\n\n# Evaluation scores\n\nWe find that this is the best performing model in the 7/8B class of LLMs on a multitude of Japanese language benchmarks.\n\nWe calculate our Japanese evaluation scores using our [lightblue-tech/japanese_llm_eval](https://github.com/lightblue-tech/japanese_llm_eval) repo.\n\n![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/2obyDbrjiNV3PGfwom6EI.png)\n\nWe also compare our Japanese model to our multilingual model using our [multilingual_mt_bench](https://github.com/Peter-Devine/multilingual_mt_bench/tree/main/fastchat/llm_judge) repo.\n\n|                 | **lightblue/suzume-llama-3-8B-japanese** | **lightblue/suzume-llama-3-8B-multilingual** | **Nexusflow/Starling-LM-7B-beta** | **gpt-3.5-turbo** |\n|-----------------|------------------------------------------|----------------------------------------------|-----------------------------------|-------------------|\n| **Japanese 🇯🇵** | 6.24                                     | 6.56                                         | 6.22                              | 7.84              |\n\nHere, we find that our multilingual model outperforms our Japanese model on the Japanese MT-Bench benchmark, indicating that our multilingual model was able to generalize better to the Japanese MT-Bench benchmark from training on more data, even if that added data was not in Japanese.\n\nNote - the discrepancy between the MT-Bench scores of the first and second evaluation of `lightblue/suzume-llama-3-8B-japanese` are due to the difference in system message of the two evaluation harnesses. The former's system message is in Japanese while the latter's is in English. \n\n# Training data\n\nWe train on three sources of data to create this model\n\n* [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja) - 669 conversations\n    * 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.\n* [openchat/openchat_sharegpt4_dataset](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json) (Japanese conversations only) - 167 conversations\n    * Conversations taken from humans talking to GPT-4\n* lightblue/tagengo-gpt4 (Japanese prompts only) (Link coming soon!) - 2,482 conversations\n    * 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`\n\n# Training config\n\n[<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)\n<details><summary>See axolotl config</summary>\n\naxolotl version: `0.4.0`\n```yaml\nbase_model: meta-llama/Meta-Llama-3-8B-Instruct\nmodel_type: LlamaForCausalLM\ntokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast\n\nload_in_8bit: false\nload_in_4bit: false\nstrict: false\n\ndatasets:\n  - path: /workspace/llm_training/axolotl/llama3-ja/openchat_megagon_lbgpt4_ja.json\n    ds_type: json # see other options below\n    type: sharegpt\n    conversation: llama-3\ndataset_prepared_path: /workspace/llm_training/axolotl/llama3-ja/prepared_openchat_megagon_lbgpt4_ja\nval_set_size: 0.01\noutput_dir: /workspace/llm_training/axolotl/llama3-ja/output_openchat_megagon_lbgpt4_ja_8B_instruct\n\nsequence_len: 8192\nsample_packing: true\npad_to_sequence_len: true\neval_sample_packing: False\n\nuse_wandb: true\nwandb_project: axolotl\nwandb_entity: peterd\nwandb_name: openchat_megagon_lbgpt4_ja_8B_instruct\n\ngradient_accumulation_steps: 2\nmicro_batch_size: 2\nnum_epochs: 1\noptimizer: paged_adamw_8bit\nlr_scheduler: cosine\nlearning_rate: 1e-5\n\ntrain_on_inputs: false\ngroup_by_length: false\nbf16: auto\nfp16:\ntf32: false\n\ngradient_checkpointing: true\ngradient_checkpointing_kwargs:\n  use_reentrant: false\nearly_stopping_patience:\nresume_from_checkpoint:\nlogging_steps: 1\nxformers_attention:\nflash_attention: true\n\nwarmup_steps: 10\nevals_per_epoch: 5\neval_table_size:\nsaves_per_epoch: 1\ndebug:\ndeepspeed: /workspace/axolotl/deepspeed_configs/zero2.json\nweight_decay: 0.0\nspecial_tokens:\n  pad_token: <|end_of_text|>\n```\n\n</details><br>\n\n\n## Training procedure\n\n### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 3\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 12\n- total_eval_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 1\n\n### Training results\n\n| Training Loss | Epoch | Step | Validation Loss |\n|:-------------:|:-----:|:----:|:---------------:|\n| 1.303         | 0.08  | 1    | 1.2664          |\n| 1.4231        | 0.23  | 3    | 1.2409          |\n| 1.1007        | 0.46  | 6    | 1.0264          |\n| 1.0635        | 0.69  | 9    | 1.0154          |\n| 1.0221        | 0.92  | 12   | 0.9555          |\n\n\n### Framework versions\n\n- Transformers 4.40.0.dev0\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.0\n\n# How to cite\n\nPlease cite [this paper](https://arxiv.org/abs/2405.12612) when referencing this model.\n\n```tex\n@article{devine2024tagengo,\n  title={Tagengo: A Multilingual Chat Dataset},\n  author={Devine, Peter},\n  journal={arXiv preprint arXiv:2405.12612},\n  year={2024}\n}\n```\n\n# Developer\n\nPeter Devine - ([ptrdvn](https://huggingface.co/ptrdvn))\n\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2405.12612",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
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  "last_modified": "2024-08-19T17:22:32.000Z",
  "created_at": "2024-08-19T15:39:06.000Z",
  "pipeline_tag": "",
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