Instructions to use froogai/NousCoder-14B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use froogai/NousCoder-14B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="froogai/NousCoder-14B-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("froogai/NousCoder-14B-AWQ")
model = AutoModelForCausalLM.from_pretrained("froogai/NousCoder-14B-AWQ")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use froogai/NousCoder-14B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "froogai/NousCoder-14B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "froogai/NousCoder-14B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/froogai/NousCoder-14B-AWQ

SGLang

How to use froogai/NousCoder-14B-AWQ 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 "froogai/NousCoder-14B-AWQ" \
    --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": "froogai/NousCoder-14B-AWQ",
		"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 "froogai/NousCoder-14B-AWQ" \
        --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": "froogai/NousCoder-14B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use froogai/NousCoder-14B-AWQ with Docker Model Runner:
```
docker model run hf.co/froogai/NousCoder-14B-AWQ
```

NousCoder-14B-AWQ

Model Description

NousCoder-14B-AWQ is a 4-bit AWQ (Activation-aware Weight Quantization) quantized version of NousResearch/NousCoder-14B.

This model specializes in competitive programming and coding tasks, achieving 67.87% Pass@1 on LiveCodeBench v6. It has been post-trained on Qwen3-14B using reinforcement learning on 24k verifiable coding problems.

Key Features

🔥 Specialized for Coding: Trained with RL on competitive programming problems
⚡ 4-bit Quantized: 66% smaller (9.4GB vs 28GB) with minimal quality loss
🚀 Fast Inference: Optimized for AWQ Marlin kernel (2-3x faster)
💻 Production Ready: Tested and verified for deployment

Model Stats

Metric	Value
Base Model	Qwen3-14B
Quantization	4-bit AWQ
Size	9.4 GB (from 28 GB)
VRAM	~6GB per GPU (2x GPUs)
Context Length	16,384 tokens
LiveCodeBench v6 Pass@1	67.87%
Training	24k coding problems (RL)

Usage

With AutoAWQ

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer

model = AutoAWQForCausalLM.from_quantized(
    "froogai/NousCoder-14B-AWQ",
    device_map="auto",
    safetensors=True,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "froogai/NousCoder-14B-AWQ",
    trust_remote_code=True,
)

# Generate code
prompt = "Write a Python function to implement binary search:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.2,
    top_p=0.95,
)

code = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(code)

With vLLM (Recommended for Production)

python -m vllm.entrypoints.openai.api_server \
    --model froogai/NousCoder-14B-AWQ \
    --quantization awq_marlin \
    --tensor-parallel-size 2 \
    --max-model-len 16384 \
    --gpu-memory-utilization 0.85 \
    --trust-remote-code

With OpenAI-Compatible API

# Start vLLM server
vllm serve froogai/NousCoder-14B-AWQ \
    --quantization awq_marlin \
    --tensor-parallel-size 2

# Make API requests
curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "froogai/NousCoder-14B-AWQ",
        "prompt": "def quicksort(arr):",
        "max_tokens": 512
    }'

Quantization Details

This model was quantized using AutoAWQ with the following configuration:

{
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM",
}

Calibration

Dataset: pileval (128 samples)
Method: Activation-aware Weight Quantization
Preservation: Coding intelligence maintained through careful calibration

Performance

Benchmarks

Benchmark	Score
LiveCodeBench v6 Pass@1	67.87%
Base Model Pass@1	60.79%
Improvement	+7.08%

Inference Speed

Hardware	Speed (tokens/sec)
2x RTX 5060 Ti (awq_marlin)	15-25
2x RTX 5060 Ti (awq)	8-12
Single A100 (awq_marlin)	40-60

Memory Usage

Configuration	VRAM Usage
2x RTX 5060 Ti (TP=2)	~6GB per GPU
Single RTX 5060 Ti	~12GB
Single A100	~6GB

Best Use Cases

This model excels at:

✅ Competitive programming problems
✅ Algorithm implementation
✅ Data structure design
✅ Code debugging and optimization
✅ Technical interview preparation
✅ LeetCode-style challenges

Recommended Generation Parameters

For coding tasks, use these settings:

temperature: 0.1-0.3 (for deterministic code)
top_p: 0.95
max_tokens: 2048+ (for complete solutions)
presence_penalty: 0.0
frequency_penalty: 0.0

Hardware Requirements

Minimum Requirements

VRAM: 12GB (single GPU) or 6GB (2 GPUs with tensor parallelism)
RAM: 24GB
Storage: 10GB

Recommended Requirements

GPUs: 2x NVIDIA RTX 5060 Ti 16GB (32GB total VRAM)
RAM: 128GB
Storage: 20GB (for model + cache)

Compatible Hardware

NVIDIA GPUs with compute capability 7.0+ (for AWQ Marlin)
CUDA 11.8+ or 12.1+
Python 3.10+

Limitations

Quantized model may have slight accuracy degradation compared to FP16
Requires AWQ-compatible libraries (AutoAWQ or vLLM)
Best performance on NVIDIA GPUs (CPU inference slower)

Training Details

Base Model

Architecture: Qwen3-14B
Parameters: 14B
License: Apache 2.0

Post-Training

Method: Reinforcement Learning
Dataset: 24k verifiable coding problems
Hardware: 48 B200 GPUs
Duration: 4 days
Framework: Atropos (NousResearch training system)

Acknowledgments

Original Model: NousResearch/NousCoder-14B
Base Model: Qwen/Qwen3-14B
Quantization: AutoAWQ library
Training Team: Joe Li (@JoeLi5050) at NousResearch

Citation

If you use this model, please cite:

@misc{nouscoder_14b_2025,
  title={NousCoder-14B: Competitive Programming AI Model},
  author={Li, Joe},
  organization={NousResearch},
  year={2025},
  month={January},
  url={https://huggingface.co/NousResearch/NousCoder-14B}
}

License

This model is licensed under the Apache 2.0 License. See the LICENSE file for details.

Model Card Authors

Quantized by: froogai

For questions or issues, please:

Open an issue on the model repository
Contact: HuggingFace profile

Note: This is a quantized version of the original model. For best performance, use the vLLM inference engine with the awq_marlin quantization backend.

Downloads last month: 206

Safetensors

Model size

15B params

Tensor type

I32

BF16

Model tree for froogai/NousCoder-14B-AWQ

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B

Finetuned

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Quantized

(20)

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