51 lines
2.8 KiB
Markdown
51 lines
2.8 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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base_model:
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- nvidia/Nemotron-Research-Reasoning-Qwen-1.5B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- code
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- reasoning
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- research
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---
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# **Nemotron-Research-Reasoning-Qwen-1.5B-GGUF**
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> [Nemotron-Research-Reasoning-Qwen-1.5B](https://huggingface.co/nvidia/Nemotron-Research-Reasoning-Qwen-1.5B) is the world’s leading 1.5B open-weight model for complex reasoning tasks such as mathematical problems, coding challenges, scientific questions, and logic puzzles. It is trained using the ProRL algorithm on a diverse and comprehensive set of datasets. Our model has achieved impressive results, outperforming Deepseek’s 1.5B model by a large margin on a broad range of tasks, including math, coding, and GPQA.
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## Model Files
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| File Name | Format | Size | Precision | Use Case |
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|-----------|--------|------|-----------|----------|
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| `Nemotron-Research-Reasoning-Qwen-1.5B.F32.gguf` | GGUF | 7.11 GB | F32 | Highest precision, research use |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.BF16.gguf` | GGUF | 3.56 GB | BF16 | High precision, balanced performance |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.F16.gguf` | GGUF | 3.56 GB | F16 | High precision, memory efficient |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q8_0.gguf` | GGUF | 1.89 GB | Q8_0 | Good quality, moderate compression |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_M.gguf` | GGUF | 1.29 GB | Q5_K_M | Balanced quality/size (recommended) |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_S.gguf` | GGUF | 1.26 GB | Q5_K_S | Good quality, smaller size |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_M.gguf` | GGUF | 1.12 GB | Q4_K_M | Good balance for most users |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_S.gguf` | GGUF | 1.07 GB | Q4_K_S | Decent quality, compact size |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_L.gguf` | GGUF | 980 MB | Q3_K_L | Lower quality, very compact |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_M.gguf` | GGUF | 924 MB | Q3_K_M | Fast inference, limited quality |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_S.gguf` | GGUF | 861 MB | Q3_K_S | Fastest inference, basic quality |
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| `Nemotron-Research-Reasoning-Qwen-1.5B.Q2_K.gguf` | GGUF | 753 MB | Q2_K | Minimal size, experimental use |
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### Quick Selection Guide
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- **For Research/Development**: Use `F32` or `BF16` for maximum accuracy
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- **For Production (Recommended)**: Use `Q5_K_M` for best quality/performance balance
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- **For Resource-Constrained Environments**: Use `Q4_K_M` or `Q4_K_S`
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- **For Edge Devices**: Use `Q3_K_M` or `Q2_K` for minimal footprint
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## Quants Usage
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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