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--- |
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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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- prithivMLmods/GCIRS-Reasoning-1.5B-R1 |
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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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- math |
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- RL |
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- science |
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--- |
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# **GCIRS-Reasoning-1.5B-R1-GGUF** |
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> **GCIRS-Reasoning-1.5B-R1** is a **research-grade reasoning model** fine-tuned from **Qwen2.5-1.5B-Instruct**, focused on **non-fictional reasoning**, **factual consistency**, and **scientific depth**. Trained with reinforcement learning using the **Big Reasoning Traces** dataset from DeepSeek, this model is tailored for complex analytical tasks and scientific rigor in high-stakes or research environments. |
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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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| `GCIRS-Reasoning-1.5B-R1.F32.gguf` | GGUF | 7.11 GB | F32 | Highest precision, research use | |
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| `GCIRS-Reasoning-1.5B-R1.BF16.gguf` | GGUF | 3.56 GB | BF16 | High precision, balanced performance | |
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| `GCIRS-Reasoning-1.5B-R1.F16.gguf` | GGUF | 3.56 GB | F16 | High precision, memory efficient | |
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| `GCIRS-Reasoning-1.5B-R1.Q8_0.gguf` | GGUF | 1.89 GB | Q8_0 | Excellent quality, moderate compression | |
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| `GCIRS-Reasoning-1.5B-R1.Q6_K.gguf` | GGUF | 1.46 GB | Q6_K | Very good quality, good compression | |
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| `GCIRS-Reasoning-1.5B-R1.Q5_K_M.gguf` | GGUF | 1.29 GB | Q5_K_M | Balanced quality/size (recommended) | |
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| `GCIRS-Reasoning-1.5B-R1.Q5_K_S.gguf` | GGUF | 1.26 GB | Q5_K_S | Good quality, smaller size | |
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| `GCIRS-Reasoning-1.5B-R1.Q4_K_M.gguf` | GGUF | 1.12 GB | Q4_K_M | Good balance for most users | |
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| `GCIRS-Reasoning-1.5B-R1.Q4_K_S.gguf` | GGUF | 1.07 GB | Q4_K_S | Decent quality, compact size | |
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| `GCIRS-Reasoning-1.5B-R1.Q3_K_L.gguf` | GGUF | 980 MB | Q3_K_L | Lower quality, very compact | |
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| `GCIRS-Reasoning-1.5B-R1.Q3_K_M.gguf` | GGUF | 924 MB | Q3_K_M | Fast inference, limited quality | |
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| `GCIRS-Reasoning-1.5B-R1.Q3_K_S.gguf` | GGUF | 861 MB | Q3_K_S | Fastest inference, basic quality | |
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| `GCIRS-Reasoning-1.5B-R1.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` or `Q6_K` for best quality/performance balance |
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- **For General Use**: Use `Q4_K_M` or `Q4_K_S` for good performance |
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- **For Resource-Constrained Environments**: Use `Q3_K_M` or `Q3_K_L` |
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- **For Edge Devices**: Use `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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