Instructions to use Minami-su/Qwen1.5-0.5B-Chat_mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minami-su/Qwen1.5-0.5B-Chat_mistral with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Minami-su/Qwen1.5-0.5B-Chat_mistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_mistral")
model = AutoModelForMultimodalLM.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_mistral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Minami-su/Qwen1.5-0.5B-Chat_mistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Minami-su/Qwen1.5-0.5B-Chat_mistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minami-su/Qwen1.5-0.5B-Chat_mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Minami-su/Qwen1.5-0.5B-Chat_mistral

SGLang

How to use Minami-su/Qwen1.5-0.5B-Chat_mistral with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Minami-su/Qwen1.5-0.5B-Chat_mistral" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minami-su/Qwen1.5-0.5B-Chat_mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Minami-su/Qwen1.5-0.5B-Chat_mistral" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minami-su/Qwen1.5-0.5B-Chat_mistral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Minami-su/Qwen1.5-0.5B-Chat_mistral with Docker Model Runner:
```
docker model run hf.co/Minami-su/Qwen1.5-0.5B-Chat_mistral
```

This is the Mistral version of Qwen1.5-0.5B-Chat model by Alibaba Cloud. The original codebase can be found at: (https://github.com/hiyouga/LLaMA-Factory/blob/main/tests/llamafy_qwen.py). I have made modifications to make it compatible with qwen1.5. This model is converted with https://github.com/Minami-su/character_AI_open/blob/main/mistral_qwen2.py

special

1.Before using this model, you need to modify modeling_mistral.py in transformers library

2.vim /root/anaconda3/envs/train/lib/python3.9/site-packages/transformers/models/mistral/modeling_mistral.py

3.find MistralAttention,

4.modify q,k,v,o bias=False ----->, bias=config.attention_bias

Before: After:

Differences between qwen2 mistral and qwen2 llamafy

Compared to qwen2 llamafy,qwen2 mistral can use sliding window attention,qwen2 mistral is faster than qwen2 llamafy, and the context length is better

Usage:


from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
tokenizer = AutoTokenizer.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_mistral")
model = AutoModelForCausalLM.from_pretrained("Minami-su/Qwen1.5-0.5B-Chat_mistral", torch_dtype="auto", device_map="auto")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

messages = [
    {"role": "user", "content": "Who are you?"}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
inputs = inputs.to("cuda")
generate_ids = model.generate(inputs,max_length=2048, streamer=streamer)

Test

load in 4bit

hf-causal (pretrained=Qwen1.5-0.5B-Chat), limit: None, provide_description: False, num_fewshot: 0, batch_size: 32
|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.2389|±  |0.0125|
|             |       |acc_norm|0.2688|±  |0.0130|
|truthfulqa_mc|      1|mc1     |0.2534|±  |0.0152|
|             |       |mc2     |0.4322|±  |0.0151|
|winogrande   |      0|acc     |0.5564|±  |0.0140|

load in 4bit

hf-causal (pretrained=Qwen1.5-0.5B-Chat_mistral), limit: None, provide_description: False, num_fewshot: 0, batch_size: 32
|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.2398|±  |0.0125|
|             |       |acc_norm|0.2705|±  |0.0130|
|truthfulqa_mc|      1|mc1     |0.2534|±  |0.0152|
|             |       |mc2     |0.4322|±  |0.0151|
|winogrande   |      0|acc     |0.5549|±  |0.0140|

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