Instructions to use cyankiwi/Jan-code-4b-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyankiwi/Jan-code-4b-AWQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="cyankiwi/Jan-code-4b-AWQ-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cyankiwi/Jan-code-4b-AWQ-4bit")
model = AutoModelForCausalLM.from_pretrained("cyankiwi/Jan-code-4b-AWQ-4bit", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyankiwi/Jan-code-4b-AWQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyankiwi/Jan-code-4b-AWQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyankiwi/Jan-code-4b-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cyankiwi/Jan-code-4b-AWQ-4bit

SGLang

How to use cyankiwi/Jan-code-4b-AWQ-4bit 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 "cyankiwi/Jan-code-4b-AWQ-4bit" \
    --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": "cyankiwi/Jan-code-4b-AWQ-4bit",
		"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 "cyankiwi/Jan-code-4b-AWQ-4bit" \
        --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": "cyankiwi/Jan-code-4b-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cyankiwi/Jan-code-4b-AWQ-4bit with Docker Model Runner:
```
docker model run hf.co/cyankiwi/Jan-code-4b-AWQ-4bit
```

Jan-code-4b-AWQ-4bit / README.md

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---
base_model: janhq/Jan-code-4b
base_model_relation: quantized
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- agent
---
<div align="left">
  <a href="https://cyan.kiwi" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-cyankiwi.png" width="800" alt="cyankiwi, the LLM inference agent for open models"></a>
</div>

<div align="left">
  <table style="border-collapse:collapse; border:none;">
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Website</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://cyan.kiwi/" target="_blank">cyan.kiwi</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Models</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/cyankiwi" target="_blank">Hugging Face</a> · <a href="https://www.modelscope.ai/organization/cyankiwi" target="_blank">ModelScope</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Connect</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://x.com/cyankiwi_ai" target="_blank">X</a> · <a href="https://www.reddit.com/r/cyankiwi/" target="_blank">Reddit</a> · <a href="mailto:ton@cyan.kiwi">Email</a></td>
    </tr>
  </table>
</div>
<br>

<div align="left">
  <img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-awq.png" width="800" alt="cyankiwi Activation-aware Weight Quantization">
</div>

<div align="left">
  <table style="border-collapse:collapse; border:none;">
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Version</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://github.com/vllm-project/llm-compressor" target="_blank">llm-compressor</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Calibration</b></td>
      <td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset" target="_blank">nvidia/Llama-Nemotron-Post-Training-Dataset</a></td>
    </tr>
    <tr style="border:none;">
      <td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Model Size</b></td>
      <td align="left" style="border:none; padding:4px 0;">2.82 GB</td>
    </tr>
  </table>
</div>
<br>

<p align="left">
  <a href="https://cyan.kiwi/workspace?prompt=Deploy%20cyankiwi/Jan-code-4b-AWQ-4bit" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-button-deploy.png" width="333" alt="Deploy on cyan.kiwi private endpoints"></a>
</p>

---

# Jan-Code-4B: a small code-tuned model

[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/janhq/jan)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://opensource.org/licenses/Apache-2.0)
[![Jan App](https://img.shields.io/badge/Powered%20by-Jan%20App-purple?style=flat\&logo=android)](https://jan.ai/)

![image](https://cdn-uploads.huggingface.co/production/uploads/657a81129ea9d52e5cbd67f7/IYCV1tqAbz43ZK__-i52A.png)

## Overview

**Jan-Code-4B** is a **code-tuned** model built on top of [Jan-v3-4B-base-instruct](https://huggingface.co/janhq/Jan-v3-4B-base-instruct). It’s designed to be a practical coding model you can run locally and iterate on quickly—useful for everyday code tasks and as a lightweight “worker” model in agentic workflows.

Compared to larger coding models, Jan-Code focuses on handling **well-scoped subtasks** reliably while keeping latency and compute requirements small.

## Intended Use

* **Lightweight coding assistant** for generation, editing, refactoring, and debugging
* **A small, fast worker model** for agent setups (e.g., as a sub-agent that produces patches/tests while a larger model plans)
* **Replace Haiku model in Claude Code setup**


## Quick Start

### Integration with Jan Apps

Jan-code is optimized for direct integration with [Jan Desktop](https://jan.ai/), select the model in the app to start using it.


### Local Deployment

**Using vLLM:**
```bash
vllm serve janhq/Jan-code-4b \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes 
    
```

**Using llama.cpp:**
```bash
llama-server --model Jan-code-4b-Q8_0.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --jinja \
    --no-context-shift
```

### Recommended Parameters
For optimal performance in agentic and general tasks, we recommend the following inference parameters:
```yaml
temperature: 0.7
top_p: 0.8
top_k: 20
```

## 🤝 Community & Support

- **Discussions**: [Hugging Face Community](https://huggingface.co/janhq/Jan-code/discussions) 
- **Jan App**: Learn more about the Jan App at [jan.ai](https://jan.ai/)

## 📄 Citation
```bibtex
Updated Soon
```

	---
	base_model: janhq/Jan-code-4b
	base_model_relation: quantized
	language:
	- en
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- agent
	---

	<div align="left">
	<a href="https://cyan.kiwi" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-cyankiwi.png" width="800" alt="cyankiwi, the LLM inference agent for open models"></a>
	</div>

	<div align="left">
	<table style="border-collapse:collapse; border:none;">
	<tr style="border:none;">
	<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Website</b></td>
	<td align="left" style="border:none; padding:4px 0;"><a href="https://cyan.kiwi/" target="_blank">cyan.kiwi</a></td>
	</tr>
	<tr style="border:none;">
	<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Models</b></td>
	<td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/cyankiwi" target="_blank">Hugging Face</a> · <a href="https://www.modelscope.ai/organization/cyankiwi" target="_blank">ModelScope</a></td>
	</tr>
	<tr style="border:none;">
	<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Connect</b></td>
	<td align="left" style="border:none; padding:4px 0;"><a href="https://x.com/cyankiwi_ai" target="_blank">X</a> · <a href="https://www.reddit.com/r/cyankiwi/" target="_blank">Reddit</a> · <a href="mailto:ton@cyan.kiwi">Email</a></td>
	</tr>
	</table>
	</div>

	<br>

	<div align="left">
	<img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-banners-awq.png" width="800" alt="cyankiwi Activation-aware Weight Quantization">
	</div>

	<div align="left">
	<table style="border-collapse:collapse; border:none;">
	<tr style="border:none;">
	<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Version</b></td>
	<td align="left" style="border:none; padding:4px 0;"><a href="https://github.com/vllm-project/llm-compressor" target="_blank">llm-compressor</a></td>
	</tr>
	<tr style="border:none;">
	<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Calibration</b></td>
	<td align="left" style="border:none; padding:4px 0;"><a href="https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset" target="_blank">nvidia/Llama-Nemotron-Post-Training-Dataset</a></td>
	</tr>
	<tr style="border:none;">
	<td align="left" style="border:none; padding:4px 24px 4px 0;"><b>Model Size</b></td>
	<td align="left" style="border:none; padding:4px 0;">2.82 GB</td>
	</tr>
	</table>
	</div>

	<br>

	<p align="left">
	<a href="https://cyan.kiwi/workspace?prompt=Deploy%20cyankiwi/Jan-code-4b-AWQ-4bit" target="_blank"><img src="https://storage.googleapis.com/cyankiwi-assets/model-card/model-card-button-deploy.png" width="333" alt="Deploy on cyan.kiwi private endpoints"></a>
	</p>

	---

	# Jan-Code-4B: a small code-tuned model

	[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue?logo=github)](https://github.com/janhq/jan)
	[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://opensource.org/licenses/Apache-2.0)
	[![Jan App](https://img.shields.io/badge/Powered%20by-Jan%20App-purple?style=flat\&logo=android)](https://jan.ai/)

	![image](https://cdn-uploads.huggingface.co/production/uploads/657a81129ea9d52e5cbd67f7/IYCV1tqAbz43ZK__-i52A.png)

	## Overview

	Jan-Code-4B is a code-tuned model built on top of [Jan-v3-4B-base-instruct](https://huggingface.co/janhq/Jan-v3-4B-base-instruct). It’s designed to be a practical coding model you can run locally and iterate on quickly—useful for everyday code tasks and as a lightweight “worker” model in agentic workflows.

	Compared to larger coding models, Jan-Code focuses on handling well-scoped subtasks reliably while keeping latency and compute requirements small.

	## Intended Use

	* Lightweight coding assistant for generation, editing, refactoring, and debugging
	* A small, fast worker model for agent setups (e.g., as a sub-agent that produces patches/tests while a larger model plans)
	* Replace Haiku model in Claude Code setup


	## Quick Start

	### Integration with Jan Apps

	Jan-code is optimized for direct integration with [Jan Desktop](https://jan.ai/), select the model in the app to start using it.


	### Local Deployment

	Using vLLM:
	```bash
	vllm serve janhq/Jan-code-4b \
	--host 0.0.0.0 \
	--port 1234 \
	--enable-auto-tool-choice \
	--tool-call-parser hermes

	```

	Using llama.cpp:
	```bash
	llama-server --model Jan-code-4b-Q8_0.gguf \
	--host 0.0.0.0 \
	--port 1234 \
	--jinja \
	--no-context-shift
	```

	### Recommended Parameters
	For optimal performance in agentic and general tasks, we recommend the following inference parameters:
	```yaml
	temperature: 0.7
	top_p: 0.8
	top_k: 20
	```

	## 🤝 Community & Support

	- Discussions: [Hugging Face Community](https://huggingface.co/janhq/Jan-code/discussions)
	- Jan App: Learn more about the Jan App at [jan.ai](https://jan.ai/)

	## 📄 Citation
	```bibtex
	Updated Soon
	```