Instructions to use IQuestLab/IQuest-Coder-V1-7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IQuestLab/IQuest-Coder-V1-7B-Base with Transformers:

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

pipe = pipeline("text-generation", model="IQuestLab/IQuest-Coder-V1-7B-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-V1-7B-Base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IQuestLab/IQuest-Coder-V1-7B-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-V1-7B-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-V1-7B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IQuestLab/IQuest-Coder-V1-7B-Base

SGLang

How to use IQuestLab/IQuest-Coder-V1-7B-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-V1-7B-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-V1-7B-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-V1-7B-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-V1-7B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IQuestLab/IQuest-Coder-V1-7B-Base with Docker Model Runner:
```
docker model run hf.co/IQuestLab/IQuest-Coder-V1-7B-Base
```

zwpride-iquestlab commited on Mar 2

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@@ -61,7 +61,7 @@ For the IQuest-Coder-V1-Thinking: We suggest using Temperature=1.0, TopP=0.95, T
 IQuest-Coder-V1 is a new family of code large language models (LLMs) designed to advance autonomous software engineering and code intelligence. Built on the innovative code-flow multi-stage training paradigm, IQuest-Coder-V1 captures the dynamic evolution of software logic, delivering state-of-the-art performance across critical dimensions:
-- **State-of-the-Art Performance**: Achieves leading results on SWE-Bench Verified, BigCodeBench, LiveCodeBench v6, and other major coding benchmarks, surpassing competitive models across agentic software engineering, competitive programming, and complex tool use.
 - **Code-Flow Training Paradigm**: Moving beyond static code representations, our models learn from repository evolution patterns, commit transitions, and dynamic code transformations to understand real-world software development processes.
 - **Dual Specialization Paths**: Bifurcated post-training delivers two specialized variants—Thinking models (utilizing reasoning-driven RL for complex problem-solving) and Instruct models (optimized for general coding assistance and instruction-following).
 - **Efficient Architecture**: The IQuest-Coder-V1-Loop variant introduces a recurrent mechanism that optimizes the trade-off between model capacity and deployment footprint. The 7B and 14B models adopt shallow architectures for faster inference speed.
@@ -205,7 +205,7 @@ claude --model IQuestCoder-V1-7B-Instruct
 | **BigCodeBench** | 0.0 | - |
 | **FullStackBench** | 0.0 | - |
 | **CruxEval** | 0.0 | - |
-| **LiveCodeBench** | 1.0 | 1.0 |
 | **Aider-Polyglot** | 0.95 | 0.85 |
 | **Mercury** | 0.2 | 0.85 |
 | **Bird** | 0.2 | 0.95 |

 IQuest-Coder-V1 is a new family of code large language models (LLMs) designed to advance autonomous software engineering and code intelligence. Built on the innovative code-flow multi-stage training paradigm, IQuest-Coder-V1 captures the dynamic evolution of software logic, delivering state-of-the-art performance across critical dimensions:
+- **Performance**: Achieves leading results on SWE-Bench Verified (76.2%), BigCodeBench (49.9%), LiveCodeBench v6 (81.1%), and other major coding benchmarks, surpassing competitive models across agentic software engineering, competitive programming, and complex tool use.
 - **Code-Flow Training Paradigm**: Moving beyond static code representations, our models learn from repository evolution patterns, commit transitions, and dynamic code transformations to understand real-world software development processes.
 - **Dual Specialization Paths**: Bifurcated post-training delivers two specialized variants—Thinking models (utilizing reasoning-driven RL for complex problem-solving) and Instruct models (optimized for general coding assistance and instruction-following).
 - **Efficient Architecture**: The IQuest-Coder-V1-Loop variant introduces a recurrent mechanism that optimizes the trade-off between model capacity and deployment footprint. The 7B and 14B models adopt shallow architectures for faster inference speed.
 | **BigCodeBench** | 0.0 | - |
 | **FullStackBench** | 0.0 | - |
 | **CruxEval** | 0.0 | - |
+| **LiveCodeBench** | 0.6 | 0.95 |
 | **Aider-Polyglot** | 0.95 | 0.85 |
 | **Mercury** | 0.2 | 0.85 |
 | **Bird** | 0.2 | 0.95 |