XavierSpycy
/

Meta-Llama-3-8B-Instruct-zh-10k-GGUF

@@ -1,258 +1,258 @@
----
-license: apache-2.0
-language:
-- en
-- zh
-base_model: meta-llama/Meta-Llama-3-8B-Instruct
-tags:
-- text-generation
-- transformers
-- lora
-- llama.cpp
-- autoawq
-- auto-gptq
-datasets:
-- llamafactory/alpaca_zh
-- llamafactory/alpaca_gpt4_zh
----
-# 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>AutoAWQ</u>
-    - [Meta-Llama-3-8B-Instruct-zh-10k-AWQ](https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-AWQ)
-- <u>AutoGPTQ</u>
-    - [Meta-Llama-3-8B-Instruct-zh-10k-GPTQ](https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-GPTQ)
-### 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。
-#### 1. How to use in transformers
-```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。
-```
-#### 2. How to use in llama.cpp / 如何在llama.cpp中使用
-```python
-# CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS # -DLLAMA_CUDA=on" \
-#  pip install llama-cpp-python \
-#  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
-# Please download the model weights first. / 请先下载模型权重。
-from llama_cpp import Llama
-llm = Llama(
-      model_path="/mnt/sdrive/jiarui/Meta-Llama-3-8B-Instruct-zh-10k-GGUF/meta-llama-3-8b-instruct-zh-10k.Q8_0.gguf",
-      n_gpu_layers=-1)
-# Alternatively / 或者
-# llm = Llama.from_pretrained(
-#     repo_id="XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-GGUF",
-#     filename="*Q8_0.gguf",
-#     verbose=False
-# )
-output = llm(
-      "Q: 你好，你是谁？A:", # Prompt
-      max_tokens=256, # Generate up to 32 tokens, set to None to generate up to the end of the context window
-      stop=["Q:", "\n"], # Stop generating just before the model would generate a new question
-      echo=True # Echo the prompt back in the output
-) # Generate a completion, can also call create_completion
-print(output['choices'][0]['text'].split("A:")[1].strip())
-# 我是一个人工智能聊天机器人，我的名字叫做“智慧助手”，我由一群程序员设计和开发的。我的主要任务就是通过与您交流来帮助您解决问题，为您提供相关的建议和支持。
-```
-#### 3. How to use with AutoAWQ / 如何与AutoAWQ一起使用
-```python
-# !pip install autoawq
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-model_id = "XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-AWQ"
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, 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))
-# 你好！我是一个人工智能助手，我的目的是帮助人们解决问题，回答问题，提供信息和建议。
-```
-#### 4. How to use with AutoGPTQ / 如何与AutoGPTQ一起使用
-```python
-# !pip install auto-gptq --no-build-isolation
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-model_id = "XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-GPTQ"
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-prompt = "什么是机器学习？"
-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))
-# 机器学习是人工智能（AI）的一个分支，它允许计算机从数据中学习并改善其性能。它是一种基于算法的方法，用于从数据中识别模式并进行预测。机器学习算法可以从数据中学习，例如文本、图像和音频，并从中获得知识和见解。
-```
-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、安全性和风险
-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.
-- Based on current observations, it fundamentally meets the standards in common sense, logic, sentiment analysis, safety, writing, code, and function calls. However, there is room for improvement in role-playing, mathematics, and handling complex tasks with the same text but different meanings.
-- 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。
-- 根据目前的观察，它在常识、逻辑、情绪分析、安全性、写作、代码和函数调用上基本达标，然而，在角色扮演、数学、复杂的同文异义等任务上有待提高。
-- 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}}
 ```

+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+tags:
+- text-generation
+- transformers
+- lora
+- llama.cpp
+- autoawq
+- auto-gptq
+datasets:
+- llamafactory/alpaca_zh
+- llamafactory/alpaca_gpt4_zh
+---
+# 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>AutoAWQ</u>
+    - [Meta-Llama-3-8B-Instruct-zh-10k-AWQ](https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-AWQ)
+- <u>AutoGPTQ</u>
+    - [Meta-Llama-3-8B-Instruct-zh-10k-GPTQ](https://huggingface.co/XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-GPTQ)
+### 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。
+#### 1. How to use in transformers
+```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。
+```
+#### 2. How to use in llama.cpp / 如何在llama.cpp中使用
+```python
+# CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS # -DLLAMA_CUDA=on" \
+#  pip install llama-cpp-python \
+#  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121
+# Please download the model weights first. / 请先下载模型权重。
+from llama_cpp import Llama
+llm = Llama(
+      model_path="/path/to/your/model/Meta-Llama-3-8B-Instruct-zh-10k-GGUF/meta-llama-3-8b-instruct-zh-10k.Q8_0.gguf",
+      n_gpu_layers=-1)
+# Alternatively / 或者
+# llm = Llama.from_pretrained(
+#     repo_id="XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-GGUF",
+#     filename="*Q8_0.gguf",
+#     verbose=False
+# )
+output = llm(
+      "Q: 你好，你是谁？A:", # Prompt
+      max_tokens=256, # Generate up to 32 tokens, set to None to generate up to the end of the context window
+      stop=["Q:", "\n"], # Stop generating just before the model would generate a new question
+      echo=True # Echo the prompt back in the output
+) # Generate a completion, can also call create_completion
+print(output['choices'][0]['text'].split("A:")[1].strip())
+# 我是一个人工智能聊天机器人，我的名字叫做“智慧助手”，我由一群程序员设计和开发的。我的主要任务就是通过与您交流来帮助您解决问题，为您提供相关的建议和支持。
+```
+#### 3. How to use with AutoAWQ / 如何与AutoAWQ一起使用
+```python
+# !pip install autoawq
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = "XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-AWQ"
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, 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))
+# 你好！我是一个人工智能助手，我的目的是帮助人们解决问题，回答问题，提供信息和建议。
+```
+#### 4. How to use with AutoGPTQ / 如何与AutoGPTQ一起使用
+```python
+# !pip install auto-gptq --no-build-isolation
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = "XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k-GPTQ"
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+prompt = "什么是机器学习？"
+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))
+# 机器学习是人工智能（AI）的一个分支，它允许计算机从数据中学习并改善其性能。它是一种基于算法的方法，用于从数据中识别模式并进行预测。机器学习算法可以从数据中学习，例如文本、图像和音频，并从中获得知识和见解。
+```
+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、安全性和风险
+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.
+- Based on current observations, it fundamentally meets the standards in common sense, logic, sentiment analysis, safety, writing, code, and function calls. However, there is room for improvement in role-playing, mathematics, and handling complex tasks with the same text but different meanings.
+- 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。
+- 根据目前的观察，它在常识、逻辑、情绪分析、安全性、写作、代码和函数调用上基本达标，然而，在角色扮演、数学、复杂的同文异义等任务上有待提高。
+- 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}}
 ```