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---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/GCIRS-Reasoning-1.5B-R1
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- math
- RL
- science
---
# **GCIRS-Reasoning-1.5B-R1-GGUF**
> **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.
## Model Files
| File Name | Format | Size | Precision | Use Case |
|-----------|--------|------|-----------|----------|
| `GCIRS-Reasoning-1.5B-R1.F32.gguf` | GGUF | 7.11 GB | F32 | Highest precision, research use |
| `GCIRS-Reasoning-1.5B-R1.BF16.gguf` | GGUF | 3.56 GB | BF16 | High precision, balanced performance |
| `GCIRS-Reasoning-1.5B-R1.F16.gguf` | GGUF | 3.56 GB | F16 | High precision, memory efficient |
| `GCIRS-Reasoning-1.5B-R1.Q8_0.gguf` | GGUF | 1.89 GB | Q8_0 | Excellent quality, moderate compression |
| `GCIRS-Reasoning-1.5B-R1.Q6_K.gguf` | GGUF | 1.46 GB | Q6_K | Very good quality, good compression |
| `GCIRS-Reasoning-1.5B-R1.Q5_K_M.gguf` | GGUF | 1.29 GB | Q5_K_M | Balanced quality/size (recommended) |
| `GCIRS-Reasoning-1.5B-R1.Q5_K_S.gguf` | GGUF | 1.26 GB | Q5_K_S | Good quality, smaller size |
| `GCIRS-Reasoning-1.5B-R1.Q4_K_M.gguf` | GGUF | 1.12 GB | Q4_K_M | Good balance for most users |
| `GCIRS-Reasoning-1.5B-R1.Q4_K_S.gguf` | GGUF | 1.07 GB | Q4_K_S | Decent quality, compact size |
| `GCIRS-Reasoning-1.5B-R1.Q3_K_L.gguf` | GGUF | 980 MB | Q3_K_L | Lower quality, very compact |
| `GCIRS-Reasoning-1.5B-R1.Q3_K_M.gguf` | GGUF | 924 MB | Q3_K_M | Fast inference, limited quality |
| `GCIRS-Reasoning-1.5B-R1.Q3_K_S.gguf` | GGUF | 861 MB | Q3_K_S | Fastest inference, basic quality |
| `GCIRS-Reasoning-1.5B-R1.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` or `Q6_K` for best quality/performance balance
- **For General Use**: Use `Q4_K_M` or `Q4_K_S` for good performance
- **For Resource-Constrained Environments**: Use `Q3_K_M` or `Q3_K_L`
- **For Edge Devices**: Use `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)