Upload NousCoder-14B-AWQ AWQ 4-bit quantized model
Browse files- .gitattributes +1 -0
- README.md +249 -0
- added_tokens.json +28 -0
- config.json +42 -0
- generation_config.json +13 -0
- merges.txt +0 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +1010 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +240 -0
- vocab.json +0 -0
.gitattributes
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README.md
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| 1 |
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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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| 30 |
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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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| 83 |
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| 84 |
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```bash
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python -m vllm.entrypoints.openai.api_server \
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| 86 |
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--model froogai/NousCoder-14B-AWQ \
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--quantization awq_marlin \
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| 88 |
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--tensor-parallel-size 2 \
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--max-model-len 16384 \
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| 90 |
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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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| 200 |
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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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| 216 |
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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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| 218 |
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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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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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| 8 |
+
"<|box_end|>": 151649,
|
| 9 |
+
"<|box_start|>": 151648,
|
| 10 |
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"<|endoftext|>": 151643,
|
| 11 |
+
"<|file_sep|>": 151664,
|
| 12 |
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"<|fim_middle|>": 151660,
|
| 13 |
+
"<|fim_pad|>": 151662,
|
| 14 |
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"<|fim_prefix|>": 151659,
|
| 15 |
+
"<|fim_suffix|>": 151661,
|
| 16 |
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"<|im_end|>": 151645,
|
| 17 |
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"<|im_start|>": 151644,
|
| 18 |
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"<|image_pad|>": 151655,
|
| 19 |
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"<|object_ref_end|>": 151647,
|
| 20 |
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"<|object_ref_start|>": 151646,
|
| 21 |
+
"<|quad_end|>": 151651,
|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
+
"<|vision_pad|>": 151654,
|
| 27 |
+
"<|vision_start|>": 151652
|
| 28 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3ForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 151643,
|
| 8 |
+
"eos_token_id": 151645,
|
| 9 |
+
"head_dim": 128,
|
| 10 |
+
"hidden_act": "silu",
|
| 11 |
+
"hidden_size": 5120,
|
| 12 |
+
"initializer_range": 0.02,
|
| 13 |
+
"intermediate_size": 17408,
|
| 14 |
+
"max_position_embeddings": 81920,
|
| 15 |
+
"max_window_layers": 40,
|
| 16 |
+
"model_type": "qwen3",
|
| 17 |
+
"num_attention_heads": 40,
|
| 18 |
+
"num_hidden_layers": 40,
|
| 19 |
+
"num_key_value_heads": 8,
|
| 20 |
+
"quantization_config": {
|
| 21 |
+
"bits": 4,
|
| 22 |
+
"group_size": 128,
|
| 23 |
+
"modules_to_not_convert": null,
|
| 24 |
+
"quant_method": "awq",
|
| 25 |
+
"version": "gemm",
|
| 26 |
+
"zero_point": true
|
| 27 |
+
},
|
| 28 |
+
"rms_norm_eps": 1e-06,
|
| 29 |
+
"rope_scaling": {
|
| 30 |
+
"factor": 2.0,
|
| 31 |
+
"original_max_position_embeddings": 40960,
|
| 32 |
+
"rope_type": "yarn"
|
| 33 |
+
},
|
| 34 |
+
"rope_theta": 1000000,
|
| 35 |
+
"sliding_window": null,
|
| 36 |
+
"tie_word_embeddings": false,
|
| 37 |
+
"torch_dtype": "bfloat16",
|
| 38 |
+
"transformers_version": "4.51.3",
|
| 39 |
+
"use_cache": false,
|
| 40 |
+
"use_sliding_window": false,
|
| 41 |
+
"vocab_size": 151936
|
| 42 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,13 @@
|
|
|
|
|
|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
151645,
|
| 6 |
+
151643
|
| 7 |
+
],
|
| 8 |
+
"pad_token_id": 151643,
|
| 9 |
+
"temperature": 0.6,
|
| 10 |
+
"top_k": 20,
|
| 11 |
+
"top_p": 0.95,
|
| 12 |
+
"transformers_version": "4.51.3"
|
| 13 |
+
}
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
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oid sha256:0b10c13e0d3854adc9a1689f7b612fcb0957c22a424be054753e733bd1f0ff04
|
| 3 |
+
size 4988339696
|
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:700d86788d9e0faa2a0bf4894957deb6e9f7cb763068710fc901475cc22d01bf
|
| 3 |
+
size 4988350264
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,1010 @@
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|
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|
| 989 |
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|
| 990 |
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|
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|
| 992 |
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|
| 993 |
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|
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|
| 995 |
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|
| 996 |
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|
| 997 |
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|
| 998 |
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|
| 999 |
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|
| 1000 |
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|
| 1001 |
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|
| 1002 |
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|
| 1003 |
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|
| 1004 |
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|
| 1005 |
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|
| 1006 |
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|
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|
| 1008 |
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"lm_head.weight": "model-00002-of-00002.safetensors"
|
| 1009 |
+
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|
| 1010 |
+
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|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,31 @@
|
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|
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|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
"<|im_end|>",
|
| 5 |
+
"<|object_ref_start|>",
|
| 6 |
+
"<|object_ref_end|>",
|
| 7 |
+
"<|box_start|>",
|
| 8 |
+
"<|box_end|>",
|
| 9 |
+
"<|quad_start|>",
|
| 10 |
+
"<|quad_end|>",
|
| 11 |
+
"<|vision_start|>",
|
| 12 |
+
"<|vision_end|>",
|
| 13 |
+
"<|vision_pad|>",
|
| 14 |
+
"<|image_pad|>",
|
| 15 |
+
"<|video_pad|>"
|
| 16 |
+
],
|
| 17 |
+
"eos_token": {
|
| 18 |
+
"content": "<|im_end|>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
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"rstrip": false,
|
| 22 |
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"single_word": false
|
| 23 |
+
},
|
| 24 |
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"pad_token": {
|
| 25 |
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"content": "<|endoftext|>",
|
| 26 |
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"lstrip": false,
|
| 27 |
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|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
}
|
| 31 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
|
| 3 |
+
size 11422654
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,240 @@
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|
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|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
| 1 |
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{
|
| 2 |
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"add_bos_token": false,
|
| 3 |
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"add_prefix_space": false,
|
| 4 |
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"added_tokens_decoder": {
|
| 5 |
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"151643": {
|
| 6 |
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"content": "<|endoftext|>",
|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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"special": true
|
| 12 |
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},
|
| 13 |
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|
| 14 |
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"content": "<|im_start|>",
|
| 15 |
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|
| 16 |
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"normalized": false,
|
| 17 |
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"rstrip": false,
|
| 18 |
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"single_word": false,
|
| 19 |
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"special": true
|
| 20 |
+
},
|
| 21 |
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"151645": {
|
| 22 |
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"content": "<|im_end|>",
|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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"special": true
|
| 28 |
+
},
|
| 29 |
+
"151646": {
|
| 30 |
+
"content": "<|object_ref_start|>",
|
| 31 |
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|
| 32 |
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|
| 33 |
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"rstrip": false,
|
| 34 |
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|
| 35 |
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"special": true
|
| 36 |
+
},
|
| 37 |
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"151647": {
|
| 38 |
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"content": "<|object_ref_end|>",
|
| 39 |
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|
| 40 |
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"normalized": false,
|
| 41 |
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"rstrip": false,
|
| 42 |
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"single_word": false,
|
| 43 |
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"special": true
|
| 44 |
+
},
|
| 45 |
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"151648": {
|
| 46 |
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"content": "<|box_start|>",
|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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"151655": {
|
| 102 |
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"content": "<|image_pad|>",
|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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"special": true
|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
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|
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
+
"lstrip": false,
|
| 176 |
+
"normalized": false,
|
| 177 |
+
"rstrip": false,
|
| 178 |
+
"single_word": false,
|
| 179 |
+
"special": false
|
| 180 |
+
},
|
| 181 |
+
"151665": {
|
| 182 |
+
"content": "<tool_response>",
|
| 183 |
+
"lstrip": false,
|
| 184 |
+
"normalized": false,
|
| 185 |
+
"rstrip": false,
|
| 186 |
+
"single_word": false,
|
| 187 |
+
"special": false
|
| 188 |
+
},
|
| 189 |
+
"151666": {
|
| 190 |
+
"content": "</tool_response>",
|
| 191 |
+
"lstrip": false,
|
| 192 |
+
"normalized": false,
|
| 193 |
+
"rstrip": false,
|
| 194 |
+
"single_word": false,
|
| 195 |
+
"special": false
|
| 196 |
+
},
|
| 197 |
+
"151667": {
|
| 198 |
+
"content": "<think>",
|
| 199 |
+
"lstrip": false,
|
| 200 |
+
"normalized": false,
|
| 201 |
+
"rstrip": false,
|
| 202 |
+
"single_word": false,
|
| 203 |
+
"special": false
|
| 204 |
+
},
|
| 205 |
+
"151668": {
|
| 206 |
+
"content": "</think>",
|
| 207 |
+
"lstrip": false,
|
| 208 |
+
"normalized": false,
|
| 209 |
+
"rstrip": false,
|
| 210 |
+
"single_word": false,
|
| 211 |
+
"special": false
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"additional_special_tokens": [
|
| 215 |
+
"<|im_start|>",
|
| 216 |
+
"<|im_end|>",
|
| 217 |
+
"<|object_ref_start|>",
|
| 218 |
+
"<|object_ref_end|>",
|
| 219 |
+
"<|box_start|>",
|
| 220 |
+
"<|box_end|>",
|
| 221 |
+
"<|quad_start|>",
|
| 222 |
+
"<|quad_end|>",
|
| 223 |
+
"<|vision_start|>",
|
| 224 |
+
"<|vision_end|>",
|
| 225 |
+
"<|vision_pad|>",
|
| 226 |
+
"<|image_pad|>",
|
| 227 |
+
"<|video_pad|>"
|
| 228 |
+
],
|
| 229 |
+
"bos_token": null,
|
| 230 |
+
"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 %}",
|
| 231 |
+
"clean_up_tokenization_spaces": false,
|
| 232 |
+
"eos_token": "<|im_end|>",
|
| 233 |
+
"errors": "replace",
|
| 234 |
+
"extra_special_tokens": {},
|
| 235 |
+
"model_max_length": 131072,
|
| 236 |
+
"pad_token": "<|endoftext|>",
|
| 237 |
+
"split_special_tokens": false,
|
| 238 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 239 |
+
"unk_token": null
|
| 240 |
+
}
|
vocab.json
ADDED
|
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|
|
|