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Model: froogai/NousCoder-14B-AWQ Source: Original Platform
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---
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license: apache-2.0
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base_model: NousResearch/NousCoder-14B
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tags:
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- awq
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- transformers
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- autotrain-llm
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- llama-cpp
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- gguf-my-repo
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- quantized
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- 4-bit
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- coding
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- competitive-programming
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- qwen
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language:
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- en
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- code
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pipeline_tag: text-generation
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---
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# NousCoder-14B-AWQ
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## Model Description
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**NousCoder-14B-AWQ** is a 4-bit AWQ (Activation-aware Weight Quantization) quantized version of [NousResearch/NousCoder-14B](https://huggingface.co/NousResearch/NousCoder-14B).
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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.
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### Key Features
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- 🔥 **Specialized for Coding**: Trained with RL on competitive programming problems
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- ⚡ **4-bit Quantized**: 66% smaller (9.4GB vs 28GB) with minimal quality loss
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- 🚀 **Fast Inference**: Optimized for AWQ Marlin kernel (2-3x faster)
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- 💻 **Production Ready**: Tested and verified for deployment
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## Model Stats
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| Metric | Value |
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|--------|-------|
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| **Base Model** | Qwen3-14B |
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| **Quantization** | 4-bit AWQ |
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| **Size** | 9.4 GB (from 28 GB) |
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| **VRAM** | ~6GB per GPU (2x GPUs) |
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| **Context Length** | 16,384 tokens |
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| **LiveCodeBench v6 Pass@1** | 67.87% |
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| **Training** | 24k coding problems (RL) |
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## Usage
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### With AutoAWQ
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```python
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from awq import AutoAWQForCausalLM
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from transformers import AutoTokenizer
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model = AutoAWQForCausalLM.from_quantized(
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"froogai/NousCoder-14B-AWQ",
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device_map="auto",
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safetensors=True,
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"froogai/NousCoder-14B-AWQ",
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trust_remote_code=True,
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)
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# Generate code
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prompt = "Write a Python function to implement binary search:"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.2,
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top_p=0.95,
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)
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code = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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print(code)
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```
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### With vLLM (Recommended for Production)
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model froogai/NousCoder-14B-AWQ \
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--quantization awq_marlin \
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--tensor-parallel-size 2 \
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--max-model-len 16384 \
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--gpu-memory-utilization 0.85 \
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--trust-remote-code
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```
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### With OpenAI-Compatible API
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```bash
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# Start vLLM server
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vllm serve froogai/NousCoder-14B-AWQ \
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--quantization awq_marlin \
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--tensor-parallel-size 2
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# Make API requests
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curl http://localhost:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "froogai/NousCoder-14B-AWQ",
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"prompt": "def quicksort(arr):",
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"max_tokens": 512
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}'
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```
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## Quantization Details
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This model was quantized using AutoAWQ with the following configuration:
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```python
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{
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"zero_point": True,
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"q_group_size": 128,
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"w_bit": 4,
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"version": "GEMM",
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}
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```
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### Calibration
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- **Dataset**: pileval (128 samples)
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- **Method**: Activation-aware Weight Quantization
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- **Preservation**: Coding intelligence maintained through careful calibration
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## Performance
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### Benchmarks
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| Benchmark | Score |
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|-----------|-------|
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| LiveCodeBench v6 Pass@1 | 67.87% |
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| Base Model Pass@1 | 60.79% |
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| **Improvement** | **+7.08%** |
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### Inference Speed
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| Hardware | Speed (tokens/sec) |
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|----------|-------------------|
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| 2x RTX 5060 Ti (awq_marlin) | 15-25 |
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| 2x RTX 5060 Ti (awq) | 8-12 |
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| Single A100 (awq_marlin) | 40-60 |
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### Memory Usage
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| Configuration | VRAM Usage |
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|---------------|------------|
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| 2x RTX 5060 Ti (TP=2) | ~6GB per GPU |
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| Single RTX 5060 Ti | ~12GB |
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| Single A100 | ~6GB |
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## Best Use Cases
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This model excels at:
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- ✅ Competitive programming problems
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- ✅ Algorithm implementation
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- ✅ Data structure design
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- ✅ Code debugging and optimization
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- ✅ Technical interview preparation
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- ✅ LeetCode-style challenges
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### Recommended Generation Parameters
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For coding tasks, use these settings:
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- **temperature**: 0.1-0.3 (for deterministic code)
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- **top_p**: 0.95
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- **max_tokens**: 2048+ (for complete solutions)
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- **presence_penalty**: 0.0
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- **frequency_penalty**: 0.0
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## Hardware Requirements
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### Minimum Requirements
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- **VRAM**: 12GB (single GPU) or 6GB (2 GPUs with tensor parallelism)
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- **RAM**: 24GB
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- **Storage**: 10GB
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### Recommended Requirements
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- **GPUs**: 2x NVIDIA RTX 5060 Ti 16GB (32GB total VRAM)
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- **RAM**: 128GB
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- **Storage**: 20GB (for model + cache)
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### Compatible Hardware
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- NVIDIA GPUs with compute capability 7.0+ (for AWQ Marlin)
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- CUDA 11.8+ or 12.1+
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- Python 3.10+
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## Limitations
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- Quantized model may have slight accuracy degradation compared to FP16
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- Requires AWQ-compatible libraries (AutoAWQ or vLLM)
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- Best performance on NVIDIA GPUs (CPU inference slower)
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## Training Details
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### Base Model
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- **Architecture**: Qwen3-14B
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- **Parameters**: 14B
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- **License**: Apache 2.0
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### Post-Training
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- **Method**: Reinforcement Learning
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- **Dataset**: 24k verifiable coding problems
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- **Hardware**: 48 B200 GPUs
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- **Duration**: 4 days
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- **Framework**: Atropos (NousResearch training system)
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## Acknowledgments
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- **Original Model**: [NousResearch/NousCoder-14B](https://huggingface.co/NousResearch/NousCoder-14B)
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- **Base Model**: [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B)
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- **Quantization**: AutoAWQ library
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- **Training Team**: Joe Li (@JoeLi5050) at NousResearch
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## Citation
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If you use this model, please cite:
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```bibtex
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@misc{nouscoder_14b_2025,
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title={NousCoder-14B: Competitive Programming AI Model},
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author={Li, Joe},
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organization={NousResearch},
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year={2025},
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month={January},
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url={https://huggingface.co/NousResearch/NousCoder-14B}
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}
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```
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## License
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This model is licensed under the **Apache 2.0 License**. See the [LICENSE](https://huggingface.co/datasets/choose-a-license) file for details.
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## Model Card Authors
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Quantized by: **froogai**
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For questions or issues, please:
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- Open an issue on the [model repository](https://huggingface.co/froogai/NousCoder-14B-AWQ/issues)
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- Contact: [HuggingFace profile](https://huggingface.co/froogai)
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---
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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.
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added_tokens.json
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config.json
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special_tokens_map.json
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user