First commit

README.md

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 ---
 license: apache-2.0
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+
+# Meta-Llama-3-8B-Instruct-zh-10k: A Llama🦙 which speaks Chinese / 一只说中文的羊驼🦙
+
+## Model Details / 模型细节
+This model, <u>`Meta-Llama-3-8B-Instruct-zh-10k`</u>, was fine-tuned from the original [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) due to its underperformance in Chinese. Utilizing the LoRa technology within the [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) utilities, this model was adapted to better handle Chinese through three epochs on three corpora: `alpaca_zh`, `alpaca_gpt4_zh`, and `oaast_sft_zh`, amounting to approximately 10,000 examples. This is reflected in the `10k` in its name.
+
+由于原模型[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)在中文上表现欠佳，于是该模型 <u>`Meta-Llama-3-8B-Instruct-zh-10k`</u> 微调自此。在[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)工具下，利用LoRa 技术，通过`alpaca_zh`、`alpaca_gpt4_zh`和`oaast_sft_zh`三个语料库上、经过三个训练轮次，我们将该模型调整得更好地掌握了中文。三个语料库共计约10,000个样本，这也是其名字中的 `10k` 的由来。
+
+For efficient inference, the model was converted to the gguf format using [llama.cpp](https://github.com/ggerganov/llama.cpp) and underwent quantization, resulting in a compact model size of about 3.18 GB, suitable for distribution across various devices.
+
+为了高效的推理，使用 [llama.cpp](https://github.com/ggerganov/llama.cpp)，我们将该模型转化为了gguf格式并量化，从而得到了一个压缩到约 3.18 GB 大小的模型，适合分发在各类设备上。
+
+### LoRa Hardware / LoRa 硬件
+- RTX 4090D x 1
+
+> [!NOTE]
+> The complete fine-tuning process took approximately 12 hours. / 完整微调过程花费约12小时。
+
+Additional fine-tuning configurations are avaiable at [Hands-On LoRa](https://github.com/XavierSpycy/hands-on-lora) or [Llama3Ops](https://github.com/XavierSpycy/llama-ops).
+
+更多微调配置可以在我的个人仓库 [Hands-On LoRa](https://github.com/XavierSpycy/hands-on-lora) 或 [Llama3Ops](https://github.com/XavierSpycy/llama-ops) 获得。
+
+### Other Models / 其他模型
+- <u>LLaMA-Factory</u>
+    - [Meta-Llama-3-8B-Instruct-zh-10k](https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k)
+
+- <u>llama.cpp</u>
+    - [Meta-Llama-3-8B-Instruct-zh-10k-GGUF](https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-GGUF)
+
+- <u>AutoAWQ</u>
+    - [Meta-Llama-3-8B-Instruct-zh-10k-AWQ](https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-AWQ)
+
+### Model Developer / 模型开发者
+- **Pretraining**: Meta
+- **Fine-tuning**: [XavierSpycy @ GitHub ](https://github.com/XavierSpycy) | [XavierSpycy @ 🤗](https://huggingface.co/XavierSpycy)
+
+- **预训练**: Meta
+- **微调**: [XavierSpycy @ GitHub](https://github.com/XavierSpycy) | [XavierSpycy @ 🤗 ](https://huggingface.co/XavierSpycy)
+
+
+### Usage / 用法
+This model can be utilized like the original <u>Meta-Llama3</u> but offers enhanced performance in Chinese.
+
+我们能够像原版的<u>Meta-Llama3</u>一样使用该模型，而它提供了提升后的中文能力。
+
+```python
+# !pip install accelerate
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k"
+
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+prompt = "你好，你是谁？"
+
+messages = [
+    {"role": "system", "content": "你是一个乐于助人的助手。"},
+    {"role": "user", "content": prompt}]
+
+input_ids = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9)
+
+response = outputs[0][input_ids.shape[-1]:]
+
+print(tokenizer.decode(response, skip_special_tokens=True))
+# 我是一个人工智能助手，旨在帮助用户解决问题和完成任务。
+# 我是一个虚拟的人工智能助手，能够通过自然语言处理技术理解用户的需求并为用户提供帮助。
+```
+
+Further details about the deployment are available in the GitHub repository [Llama3Ops: From LoRa to Deployment with Llama3](https://github.com/XavierSpycy/llama-ops).
+
+更多关于部署的细节可以在我的个人仓库 [Llama3Ops: From LoRa to Deployment with Llama3](https://github.com/XavierSpycy/llama-ops) 获得。
+
+## Ethical Considerations, Safety & Risks / 伦理考量、安全性和危险
+Please refer to [Meta Llama 3's Ethical Considerations](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct#ethical-considerations-and-limitations) for more information. Key points include bias monitoring, responsible usage guidelines, and transparency in model limitations.
+
+请参考 [Meta Llama 3's Ethical Considerations](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct#ethical-considerations-and-limitations)，以获取更多细节。关键点包括偏见监控、负责任的使用指南和模型限制的透明度。
+
+## Limitations / 局限性
+- The comprehensive abilities of the model have not been fully tested. 
+
+- While it performs smoothly in Chinese conversations, further benchmarks are required to evaluate its full capabilities. The quality and quantity of the Chinese corpora used may also limit model outputs. 
+
+- Additionally, catastrophic forgetting in the fine-tuned model has not been evaluated.
+
+- 该模型的全面的能力尚未全部测试。
+
+- 尽管它在中文对话中表现流畅，但需要更多的测评以评估其完整的能力。中文语料库的质量和数量可能都会对模型输出有所制约。
+
+- 另外，微调模型中的灾难性遗忘尚未评估。
+
+## Acknowledgements / 致谢
+We thank Meta for their open-source contributions, which have greatly benefited the developer community, and acknowledge the collaborative efforts of developers in enhancing this community. 
+
+我们感谢 Meta 的开源贡献，这极大地帮助了开发者社区，同时，也感谢致力于提升社区的开发者们的努力。
+
+## References / 参考资料
+
+```
+@article{llama3modelcard,
+title={Llama 3 Model Card},
+author={AI@Meta},
+year={2024},
+url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}}
+
+@inproceedings{zheng2024llamafactory,
+  title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
+  author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},
+  booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
+  address={Bangkok, Thailand},
+  publisher={Association for Computational Linguistics},
+  year={2024},
+  url={http://arxiv.org/abs/2403.13372}}
+```

config.json

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+{
+  "_name_or_path": "Meta-Llama-3-8B-Instruct-zh-10k",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "bits": 4,
+    "checkpoint_format": "gptq",
+    "damp_percent": 0.01,
+    "desc_act": true,
+    "group_size": 128,
+    "model_file_base_name": null,
+    "model_name_or_path": null,
+    "quant_method": "gptq",
+    "static_groups": false,
+    "sym": true,
+    "true_sequential": true
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

gptq_model-4bit-128g.safetensors

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+oid sha256:9ecbdf87ba4c17ac929e9024e035e03ebdac014c04f53ac580f52b71ce05a31d
+size 5732943784

quantize_config.json

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+{
+  "bits": 4,
+  "group_size": 128,
+  "damp_percent": 0.01,
+  "desc_act": true,
+  "static_groups": false,
+  "sym": true,
+  "true_sequential": true,
+  "model_name_or_path": null,
+  "model_file_base_name": null,
+  "quant_method": "gptq",
+  "checkpoint_format": "gptq"
+}