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Model: prithivMLmods/Research-Reasoning-Qwen-F32-GGUF
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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- nvidia/Nemotron-Research-Reasoning-Qwen-1.5B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- code
+- reasoning
+- research
+---
+
+# **Nemotron-Research-Reasoning-Qwen-1.5B-GGUF**
+
+> [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. 
+
+## Model Files
+
+| File Name | Format | Size | Precision | Use Case |
+|-----------|--------|------|-----------|----------|
+| `Nemotron-Research-Reasoning-Qwen-1.5B.F32.gguf` | GGUF | 7.11 GB | F32 | Highest precision, research use |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.BF16.gguf` | GGUF | 3.56 GB | BF16 | High precision, balanced performance |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.F16.gguf` | GGUF | 3.56 GB | F16 | High precision, memory efficient |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q8_0.gguf` | GGUF | 1.89 GB | Q8_0 | Good quality, moderate compression |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_M.gguf` | GGUF | 1.29 GB | Q5_K_M | Balanced quality/size (recommended) |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_S.gguf` | GGUF | 1.26 GB | Q5_K_S | Good quality, smaller size |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_M.gguf` | GGUF | 1.12 GB | Q4_K_M | Good balance for most users |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_S.gguf` | GGUF | 1.07 GB | Q4_K_S | Decent quality, compact size |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_L.gguf` | GGUF | 980 MB | Q3_K_L | Lower quality, very compact |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_M.gguf` | GGUF | 924 MB | Q3_K_M | Fast inference, limited quality |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_S.gguf` | GGUF | 861 MB | Q3_K_S | Fastest inference, basic quality |
+| `Nemotron-Research-Reasoning-Qwen-1.5B.Q2_K.gguf` | GGUF | 753 MB | Q2_K | Minimal size, experimental use |
+
+### Quick Selection Guide
+
+- **For Research/Development**: Use `F32` or `BF16` for maximum accuracy
+- **For Production (Recommended)**: Use `Q5_K_M` for best quality/performance balance
+- **For Resource-Constrained Environments**: Use `Q4_K_M` or `Q4_K_S`
+- **For Edge Devices**: Use `Q3_K_M` or `Q2_K` for minimal footprint
+
+## Quants Usage 
+
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

config.json

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+{
+    "model_type": "qwen2"
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}