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Model: prithivMLmods/OpenRHO-2B-Thinker-GGUF
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---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/OpenRHO-2B-Thinker
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---
# **OpenRHO-2B-Thinker-GGUF**
> **OpenRHO-2B-Thinker** is a **general-purpose reasoning model** designed to enhance the cognitive abilities of **edge-deployed large language models (LLMs)** through **reinforcement learning (RL)**. Fine-tuned from **Qwen2-1.5B-Instruct** using the **QwQ distill dataset**, it delivers refined improvements in logical reasoning, structured problem-solving, and lightweight coding — making it highly efficient for **resource-constrained environments**.
## Model Files
| File Name | Size | Quantization | Format | Description |
| -------------------------------- | ------- | ------------ | ------ | ----------------------------- |
| `OpenRHO-2B-Thinker.BF16.gguf` | 3.56 GB | BF16 | GGUF | BFloat16 precision version |
| `OpenRHO-2B-Thinker.F16.gguf` | 3.56 GB | FP16 | GGUF | Float16 precision version |
| `OpenRHO-2B-Thinker.F32.gguf` | 7.11 GB | FP32 | GGUF | Float32 precision version |
| `OpenRHO-2B-Thinker.Q2_K.gguf` | 753 MB | Q2\_K | GGUF | 2-bit quantized (K variant) |
| `OpenRHO-2B-Thinker.Q3_K_M.gguf` | 924 MB | Q3\_K\_M | GGUF | 3-bit quantized (K M variant) |
| `OpenRHO-2B-Thinker.Q4_K_M.gguf` | 1.12 GB | Q4\_K\_M | GGUF | 4-bit quantized (K M variant) |
| `OpenRHO-2B-Thinker.Q4_K_S.gguf` | 1.07 GB | Q4\_K\_S | GGUF | 4-bit quantized (K S variant) |
| `OpenRHO-2B-Thinker.Q5_K_M.gguf` | 1.29 GB | Q5\_K\_M | GGUF | 5-bit quantized (K M variant) |
| `OpenRHO-2B-Thinker.Q5_K_S.gguf` | 1.26 GB | Q5\_K\_S | GGUF | 5-bit quantized (K S variant) |
| `OpenRHO-2B-Thinker.Q6_K.gguf` | 1.46 GB | Q6\_K | GGUF | 6-bit quantized (K variant) |
| `OpenRHO-2B-Thinker.Q8_0.gguf` | 1.89 GB | Q8\_0 | GGUF | 8-bit quantized |
| `.gitattributes` | 2.24 kB | — | — | Git LFS tracking file |
| `config.json` | 31 B | — | — | Configuration file |
| `README.md` | 670 B | — | — | Model documentation |
---
## Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_S.gguf) | Q3_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.IQ4_XS.gguf) | IQ4_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_S.gguf) | Q5_K_S | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_M.gguf) | Q5_K_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q6_K.gguf) | Q6_K | 0.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q8_0.gguf) | Q8_0 | 0.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.f16.gguf) | f16 | 1.6 | 16 bpw, overkill |
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": "qwen2"
}

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