Instructions to use IQuestLab/IQuest-Q1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IQuestLab/IQuest-Q1 with Transformers:

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

pipe = pipeline("text-generation", model="IQuestLab/IQuest-Q1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IQuestLab/IQuest-Q1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IQuestLab/IQuest-Q1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IQuestLab/IQuest-Q1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IQuestLab/IQuest-Q1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IQuestLab/IQuest-Q1

SGLang

How to use IQuestLab/IQuest-Q1 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 "IQuestLab/IQuest-Q1" \
    --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": "IQuestLab/IQuest-Q1",
		"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 "IQuestLab/IQuest-Q1" \
        --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": "IQuestLab/IQuest-Q1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IQuestLab/IQuest-Q1 with Docker Model Runner:
```
docker model run hf.co/IQuestLab/IQuest-Q1
```

yunyin007 commited on Sep 28

Commit

5b2103a

verified ·

1 Parent(s): b58b64b

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -3

README.md CHANGED Viewed

@@ -36,13 +36,11 @@ library_name: transformers
 <p align="center">
   <img src="assets/IQuest-Q1-Training-Pipeline.png" width="100%">
-  <br>
   <em>Training pipeline of IQuest-Q1.</em>
 </p>
 <p align="center">
   <img src="assets/IQuest-Q1-Post-Training-Pipeline.png" width="100%">
-  <br>
   <em>IQuest-Q1 Post-Training Pipeline.</em>
 </p>
@@ -69,7 +67,6 @@ library_name: transformers
 <p align="center">
   <img src="assets/IQuest-Q1_Performance.png" width="100%">
-  <br>
   <em>IQuest-Q1 performance across benchmarks. DeepSeek-V4-Flash/Pro refer to the official 0731/0813 releases, respectively. Humanity's Last Exam is reported without tools. IQuest-CLIBench is our in-house benchmark for CLI user experience.</em>
 </p>

 <p align="center">
   <img src="assets/IQuest-Q1-Training-Pipeline.png" width="100%">
   <em>Training pipeline of IQuest-Q1.</em>
 </p>
 <p align="center">
   <img src="assets/IQuest-Q1-Post-Training-Pipeline.png" width="100%">
   <em>IQuest-Q1 Post-Training Pipeline.</em>
 </p>
 <p align="center">
   <img src="assets/IQuest-Q1_Performance.png" width="100%">
   <em>IQuest-Q1 performance across benchmarks. DeepSeek-V4-Flash/Pro refer to the official 0731/0813 releases, respectively. Humanity's Last Exam is reported without tools. IQuest-CLIBench is our in-house benchmark for CLI user experience.</em>
 </p>