Instructions to use IQuestLab/IQuest-Coder-V1-14B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IQuestLab/IQuest-Coder-V1-14B-Base with Transformers:

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

pipe = pipeline("text-generation", model="IQuestLab/IQuest-Coder-V1-14B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IQuestLab/IQuest-Coder-V1-14B-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IQuestLab/IQuest-Coder-V1-14B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IQuestLab/IQuest-Coder-V1-14B-Base"
# 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-Coder-V1-14B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IQuestLab/IQuest-Coder-V1-14B-Base

SGLang

How to use IQuestLab/IQuest-Coder-V1-14B-Base 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-Coder-V1-14B-Base" \
    --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-Coder-V1-14B-Base",
		"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-Coder-V1-14B-Base" \
        --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-Coder-V1-14B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IQuestLab/IQuest-Coder-V1-14B-Base with Docker Model Runner:
```
docker model run hf.co/IQuestLab/IQuest-Coder-V1-14B-Base
```

zwpride-iquestlab commited on Mar 1

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@@ -14,14 +14,14 @@ library_name: transformers
 <p align="center">
   📘 <a href="https://iquestlab.github.io">Blog (2026-01-01)</a >
   &nbsp;•&nbsp;
-  📘 <a href="https://iquestlab.github.io">Blog (2026-03-02)</a >
   &nbsp;•&nbsp;
   📄 <a href="https://github.com/IQuestLab/IQuest-Coder-V1/blob/main/papers/IQuest_Coder_Technical_Report.pdf">Technical Report</a >
 </p >
 # IQuest-Coder-V1 Model Family Update
-🚀🚀🚀 [IQuest-Coder-V1 Model Family Update](https://iquestlab.github.io/release-1.0-2602/index.html): Released 7B & 14B Family Models, 40B-Thinking and 40B-Loop-Thinking, specially optimized for tool use, CLI agents (Like `Claude Code` and `OpenCode`) & HTML/SVG generation, all with 128K context, now on Hugging Face!
 ## 7B Models

 <p align="center">
   📘 <a href="https://iquestlab.github.io">Blog (2026-01-01)</a >
   &nbsp;•&nbsp;
+  📘 <a href="https://iquestlab.github.io/release-1.0-2603/index.html">Blog (2026-03-02)</a >
   &nbsp;•&nbsp;
   📄 <a href="https://github.com/IQuestLab/IQuest-Coder-V1/blob/main/papers/IQuest_Coder_Technical_Report.pdf">Technical Report</a >
 </p >
 # IQuest-Coder-V1 Model Family Update
+🚀🚀🚀 [IQuest-Coder-V1 Model Family Update](https://iquestlab.github.io/release-1.0-2603/index.html): Released 7B & 14B Family Models, 40B-Thinking and 40B-Loop-Thinking, specially optimized for tool use, CLI agents (Like `Claude Code` and `OpenCode`) & HTML/SVG generation, all with 128K context, now on Hugging Face!
 ## 7B Models