Update model card

README.md

@@ -1,5 +1,19 @@
 ---
 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🦙
@@ -46,6 +60,7 @@ This model can be utilized like the original <u>Meta-Llama3</u> but offers enhan
 
 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
 
@@ -83,11 +98,120 @@ print(tokenizer.decode(response, skip_special_tokens=True))
 # 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、安全性和危险
+## 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。
@@ -97,12 +221,16 @@ Please refer to [Meta Llama 3's Ethical Considerations](https://huggingface.co/m
 
 - 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