Instructions to use StableQuant/Qwen-Templates-Rebuild-Project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StableQuant/Qwen-Templates-Rebuild-Project with Transformers:

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

pipe = pipeline("text-generation", model="StableQuant/Qwen-Templates-Rebuild-Project")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("StableQuant/Qwen-Templates-Rebuild-Project", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use StableQuant/Qwen-Templates-Rebuild-Project with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StableQuant/Qwen-Templates-Rebuild-Project"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StableQuant/Qwen-Templates-Rebuild-Project",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/StableQuant/Qwen-Templates-Rebuild-Project

SGLang

How to use StableQuant/Qwen-Templates-Rebuild-Project 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 "StableQuant/Qwen-Templates-Rebuild-Project" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StableQuant/Qwen-Templates-Rebuild-Project",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "StableQuant/Qwen-Templates-Rebuild-Project" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StableQuant/Qwen-Templates-Rebuild-Project",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use StableQuant/Qwen-Templates-Rebuild-Project with Docker Model Runner:
```
docker model run hf.co/StableQuant/Qwen-Templates-Rebuild-Project
```

StableQuant commited on 2 days ago

Commit

aaa9805

verified ·

1 Parent(s): 224652f

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -23,8 +23,8 @@ TL:DR Download your fixed and finally working Qwen3.5 and Qwen3.6 jinja chat-tem
 You can use this template for any Qwen3 model (only Qwen3-Coder has another template format)
 Compatible with Qwen3, Qwen3.5 and Qwen3.6 in any OpenWeight size yet released!
-#### Update: V1.1.2 is out in the wild now!
-Additional fixes to tool calling have been done, verified correct developer role handling and other improvements. I would consider this a first stable version.
 #### OpenCode:
 If using OpenCode do notice they have an open bug for showing thinking content as plain text. You can look it up on Github. Its open since 4 months. Meanwhile you can use the llama.cpp Server switch to hide thinking completely(but still let the model think) using the additonal llama.cpp parameter:

 You can use this template for any Qwen3 model (only Qwen3-Coder has another template format)
 Compatible with Qwen3, Qwen3.5 and Qwen3.6 in any OpenWeight size yet released!
+#### Update: V1.1.5 is out in the wild now!
+Additional fixes to tool calling have been done, verified correct developer role handling and other improvements.
 #### OpenCode:
 If using OpenCode do notice they have an open bug for showing thinking content as plain text. You can look it up on Github. Its open since 4 months. Meanwhile you can use the llama.cpp Server switch to hide thinking completely(but still let the model think) using the additonal llama.cpp parameter: