OpenG
Collection
Math, Code, Reasoning – Edge Device LLMs
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4 items
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Updated
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2
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.
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 |
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Link | Type | Size/GB | Notes |
---|---|---|---|
GGUF | Q2_K | 0.4 | |
GGUF | Q3_K_S | 0.5 | |
GGUF | Q3_K_M | 0.5 | lower quality |
GGUF | Q3_K_L | 0.5 | |
GGUF | IQ4_XS | 0.6 | |
GGUF | Q4_K_S | 0.6 | fast, recommended |
GGUF | Q4_K_M | 0.6 | fast, recommended |
GGUF | Q5_K_S | 0.6 | |
GGUF | Q5_K_M | 0.7 | |
GGUF | Q6_K | 0.7 | very good quality |
GGUF | Q8_0 | 0.9 | fast, best quality |
GGUF | f16 | 1.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
16-bit
32-bit
Base model
Qwen/Qwen2-1.5B-Instruct