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---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/OpenScienceReasoning-Qwen-e10
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- medical
- science
---
# **OpenScienceReasoning-Qwen-e10-GGUF**
> OpenScienceReasoning-Qwen-e10 is a high-efficiency scientific reasoning model fine-tuned from Qwen3-1.7B using the nvidia/OpenScienceReasoning-2 dataset, encompassing 10,000 curated science and math entries that strengthen analytical problem-solving, chain-of-thought exploration, and code reasoning. The model excels at hybrid symbolic-AI thinking by performing structured logic, scientific derivations, multi-language coding, and generating outputs in formats such as LaTeX, Markdown, JSON, CSV, and YAML, making it ideal for research, education, and technical documentation on mid-range GPUs and edge clusters. Optimized for STEM applications, OpenScienceReasoning-Qwen-e10 delivers robust performance for tutoring, research assistance, and structured data generation while maintaining a lightweight deployment footprint.
## Model Files
| File Name | Quant Type | File Size |
| - | - | - |
| OpenScienceReasoning-Qwen-e10.BF16.gguf | BF16 | 3.45 GB |
| OpenScienceReasoning-Qwen-e10.F16.gguf | F16 | 3.45 GB |
| OpenScienceReasoning-Qwen-e10.F32.gguf | F32 | 6.89 GB |
| OpenScienceReasoning-Qwen-e10.Q2_K.gguf | Q2_K | 778 MB |
| OpenScienceReasoning-Qwen-e10.Q3_K_L.gguf | Q3_K_L | 1 GB |
| OpenScienceReasoning-Qwen-e10.Q3_K_M.gguf | Q3_K_M | 940 MB |
| OpenScienceReasoning-Qwen-e10.Q3_K_S.gguf | Q3_K_S | 867 MB |
| OpenScienceReasoning-Qwen-e10.Q4_0.gguf | Q4_0 | 1.05 GB |
| OpenScienceReasoning-Qwen-e10.Q4_1.gguf | Q4_1 | 1.14 GB |
| OpenScienceReasoning-Qwen-e10.Q4_K.gguf | Q4_K | 1.11 GB |
| OpenScienceReasoning-Qwen-e10.Q4_K_M.gguf | Q4_K_M | 1.11 GB |
| OpenScienceReasoning-Qwen-e10.Q4_K_S.gguf | Q4_K_S | 1.06 GB |
| OpenScienceReasoning-Qwen-e10.Q5_0.gguf | Q5_0 | 1.23 GB |
| OpenScienceReasoning-Qwen-e10.Q5_1.gguf | Q5_1 | 1.32 GB |
| OpenScienceReasoning-Qwen-e10.Q5_K.gguf | Q5_K | 1.26 GB |
| OpenScienceReasoning-Qwen-e10.Q5_K_M.gguf | Q5_K_M | 1.26 GB |
| OpenScienceReasoning-Qwen-e10.Q5_K_S.gguf | Q5_K_S | 1.23 GB |
| OpenScienceReasoning-Qwen-e10.Q6_K.gguf | Q6_K | 1.42 GB |
| OpenScienceReasoning-Qwen-e10.Q8_0.gguf | Q8_0 | 1.83 GB |
## 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)

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{
"model_type": "qwen3"
}

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