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tinyopsec/Skywork-OR1-7B-GGUF overview

Skywork OR1 7B GGUF GGUF quantizations of Skywork/Skywork OR1 7B https://huggingface.co/Skywork/Skywork OR1 7B . Description Skywork OR1 7B Open Reasoner 1 is …

ggufqwen2reasoningmathcodereinforcement-learningtext-generationenbase_model:Skywork/Skywork-OR1-7Bbase_model:quantized:Skywork/Skywork-OR1-7Blicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~2.81 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
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Repository Files & Downloads

11 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
model_f16.ggufGGUFF1614.19 GBDownload
model_q2_k.ggufGGUFQ2_K2.81 GBDownload
model_q3_k_l.ggufGGUFQ3_K_L3.81 GBDownload
model_q3_k_m.ggufGGUFQ3_K_M3.55 GBDownload
model_q3_k_s.ggufGGUFQ3_K_S3.25 GBDownload
model_q4_k_m.ggufGGUFQ4_K_M4.36 GBDownload
model_q4_k_s.ggufGGUFQ4_K_S4.15 GBDownload
model_q5_k_m.ggufGGUFQ5_K_M5.07 GBDownload
model_q5_k_s.ggufGGUFQ5_K_S4.95 GBDownload
model_q6_k.ggufGGUFQ6_K5.82 GBDownload
model_q8_0.ggufGGUFQ8_07.54 GBDownload

Model Details

Model IDtinyopsec/Skywork-OR1-7B-GGUF
Authortinyopsec
Pipelinetext-generation
Licenseapache-2.0
Base modelSkywork/Skywork-OR1-7B
Last modified2026-09-15T16:54:50.000Z

Model README

---

license: apache-2.0

base_model: Skywork/Skywork-OR1-7B

tags:

- gguf

- qwen2

- reasoning

- math

- code

- reinforcement-learning

language:

- en

pipeline_tag: text-generation

library_name: gguf

---

Skywork-OR1-7B GGUF

GGUF quantizations of Skywork/Skywork-OR1-7B.

Description

Skywork-OR1-7B (Open Reasoner 1) is a general-purpose math and code reasoning model trained with large-scale rule-based reinforcement learning using a customized GRPO algorithm. It is based on DeepSeek-R1-Distill-Qwen-7B and trained on 110K math problems and 14K coding questions with model-aware difficulty estimation, offline/online filtering, rejection sampling, multi-stage training pipeline, and adaptive entropy control.

Quantization Files

| File | Bits | Size | Use Case |

|------|------|------|----------|

| model_f16.gguf | 16 | ~15.2 GB | Maximum quality, reference |

| model_q8_0.gguf | 8 | ~8.5 GB | Best quality, near-lossless |

| model_q6_k.gguf | 6 | ~6.6 GB | High quality |

| model_q5_k_m.gguf | 5 | ~5.7 GB | Balanced quality |

| model_q5_k_s.gguf | 5 | ~5.5 GB | Balanced quality, smaller |

| model_q4_k_m.gguf | 4 | ~4.9 GB | Good quality, recommended |

| model_q4_k_s.gguf | 4 | ~4.7 GB | Good quality, smaller |

| model_q3_k_l.gguf | 3 | ~4.0 GB | Low quality, small |

| model_q3_k_m.gguf | 3 | ~3.7 GB | Low quality, smaller |

| model_q3_k_s.gguf | 3 | ~3.5 GB | Very low quality |

| model_q2_k.gguf | 2 | ~3.0 GB | Lowest quality, minimum size |

VRAM Requirements

| Quantization | VRAM |

|---|---|

| F16 | ~16 GB |

| Q8_0 | ~9 GB |

| Q6_K | ~7 GB |

| Q5_K_M | ~6 GB |

| Q4_K_M | ~5 GB |

| Q3_K_M | ~4 GB |

| Q2_K | ~3.5 GB |

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "<your prompt>" -n 512

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=32768)
output = llm("<your prompt>", max_tokens=512)
print(output["choices"][0]["text"])

LM Studio

Load any .gguf file directly in LM Studio via Load Model.

Ollama

ollama run hf.co/tinyopsec/Skywork-OR1-7B-GGUF:Q4_K_M

Original Model

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