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

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