Update README.md

README.md

@@ -1,23 +1,25 @@
 ---
-license: apache-2.0
 language:
 - en
 pipeline_tag: text-generation
 tags:
+- instruct
 - chat
+license: apache-2.0
 ---
 
 # Qwen2-0.5B-Instruct-GGUF
 
 ## Introduction
 
-Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 0.5B Qwen2 model.
+Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model (57B-A14B). This repo contains the instruction-tuned 0.5B Qwen2 model.
 
 Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc.
 
 For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/).
 
-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`.
+In this repo, we provide 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.
@@ -27,18 +29,51 @@ We pretrained the models with a large amount of data, and we post-trained the mo
 
 
 ## 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/Qwen2-0.5B-Instruct-GGUF qwen2-0_5b-instruct-q8_0.gguf --local-dir . --local-dir-use-symlinks False
+huggingface-cli download Qwen/Qwen2-0.5B-Instruct-GGUF qwen2-0.5b-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 qwen2-0_5b-instruct-q8_0.gguf -n 512 --color -i -cml -f prompts/chat-with-qwen.txt
+With the upgrade of APIs of llama.cpp, `llama-gguf-split` is equivalent to the previous `gguf-split`. 
+For the arguments of this command, the first is the path to the first split GGUF file, and the second is the path to the output GGUF file.
+
+
+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 qwen2-0.5b-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 qwen2-0.5b-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