Update README.md

README.md

@@ -5,6 +5,7 @@ language:
 pipeline_tag: text-generation
 tags:
 - chat
+- instruct
 ---
 
 # Qwen2-1.5B-Instruct-GGUF
@@ -17,7 +18,7 @@ Compared with the state-of-the-art opensource language models, including the pre
 
 For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/) and [GitHub](https://github.com/QwenLM/Qwen2).
 
-In this repo, we provide `fp16` model and quantized models in the GGUF formats, including `q5_0`, `q5_k_m`, `q6_k` and `q8_0`.
+In this repo, we provide `fp16` model and quantized models in the GGUF formats, including `q2_k`, `q3_k_m`, `q4_0`, `q4_k_m`, `q5_0`, `q5_k_m`, `q6_k` and `q8_0`.
 
 ## Model Details
 Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.
@@ -25,22 +26,51 @@ Qwen2 is a language model series including decoder language models of different
 ## Training details
 We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
 
-
 ## Requirements
-We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide.
+We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide. We follow the latest version of llama.cpp. 
+In the following demonstration, we assume that you are running commands under the repository `llama.cpp`.
 
 
 ## How to use
 Cloning the repo may be inefficient, and thus you can manually download the GGUF file that you need or use `huggingface-cli` (`pip install huggingface_hub`) as shown below:
 ```shell
-huggingface-cli download Qwen/Qwen2-1.5B-Instruct-GGUF qwen2-1_5b-instruct-q8_0.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download Qwen/Qwen2-1.5B-Instruct-GGUF qwen2-1.5b-instruct-q5_k_m.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
-We demonstrate how to use `llama.cpp` to run Qwen2:
-```shell
-./main -m qwen2-1_5b-instruct-q8_0.gguf -n 512 --color -i -cml -f prompts/chat-with-qwen.txt
+To run Qwen2, you can use `llama-cli` (the previous `main`) or `llama-server` (the previous `server`). 
+We recommend using the `llama-server` as it is simple and compatible with OpenAI API. For example:
+
+```bash
+./llama-server -m qwen2-1.5b-instruct-q5_k_m.gguf
 ```
 
+Then it is easy to access the deployed service with OpenAI API:
+
+```python
+import openai
+
+client = openai.OpenAI(
+    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
+    api_key = "sk-no-key-required"
+)
+
+completion = client.chat.completions.create(
+    model="qwen",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "tell me something about michael jordan"}
+    ]
+)
+print(completion.choices[0].message.content)
+```
+
+If you choose to use `llama-cli`, pay attention to the removal of `-cml` for the ChatML template. Instead you should use `--in-prefix` and `--in-suffix` to tackle this problem.
+
+```bash
+./llama-cli -m qwen2-1.5b-instruct-q5_k_m.gguf -n 512 -co -i -if -f prompts/chat-with-qwen.txt --in-prefix "<|im_start|>user\n" --in-suffix "<|im_end|>\n<|im_start|>assistant\n"
+```
+
+
 ## Citation
 
 If you find our work helpful, feel free to give us a cite.
@@ -50,4 +80,4 @@ If you find our work helpful, feel free to give us a cite.
   title={Qwen2 Technical Report},
   year={2024}
 }
-```
+```