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ermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF overview

Granite 4.1 8B SFT Claude Opus Reasoning Unsloth GGUF GGUF quantizations of Granite 4.1 8B SFT Claude Opus Reasoning Unsloth https://huggingface.co/ermiaazarkh…

ggufllama.cppquantizedgranitereasoningunslothtext-generationendataset:ermiaazarkhalili/claude-reasoning-distillationbase_model:ermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unslothbase_model:quantized:ermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unslothlicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~3.18 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
92
Likes
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Pipeline
text-generation

Repository Files & Downloads

6 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q2_k.ggufGGUFGGUF3.18 GBDownload
granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q3_k_m.ggufGGUFGGUF4.05 GBDownload
granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q4_k_m.ggufGGUFGGUF4.98 GBDownload
granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q5_k_m.ggufGGUFGGUF5.82 GBDownload
granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q6_k.ggufGGUFGGUF6.72 GBDownload
granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q8_0.ggufGGUFGGUF8.70 GBDownload

Model Details

Model IDermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF
Authorermiaazarkhalili
Pipelinetext-generation
Licenseapache-2.0
Base modelermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth
Last modified2026-06-22T11:07:17.000Z

Model README

---

license: apache-2.0

language:

- en

library_name: gguf

pipeline_tag: text-generation

tags:

- gguf

- llama.cpp

- quantized

- granite

- reasoning

- unsloth

- text-generation

base_model: ermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth

datasets:

- ermiaazarkhalili/claude-reasoning-distillation

---

Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF

GGUF quantizations of Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth for CPU and edge inference with llama.cpp, Ollama, LM Studio, and other GGUF runtimes.

This model is a fine-tune of Granite 4.1-8B for reasoning distillation (chain-of-thought), trained with Unsloth on claude-reasoning-distillation.

Available Quantizations

| Quant | Size | Recommended Use | File |

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

| Q2_K | 3.41 GB | Smallest, lowest quality — quick tests / very constrained devices | granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q2_k.gguf |

| Q3_K_M | 4.35 GB | Small, acceptable quality | granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q3_k_m.gguf |

| Q4_K_M | 5.35 GB | Recommended — best size/quality balance | granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q4_k_m.gguf |

| Q5_K_M | 6.25 GB | High quality, larger | granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q5_k_m.gguf |

| Q6_K | 7.22 GB | Very high quality, near-fp16 | granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q6_k.gguf |

| Q8_0 | 9.35 GB | Near-lossless, largest | granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q8_0.gguf |

Q4_K_M is the recommended default for most users.

Usage

Download a single quant

pip install -U "huggingface_hub[cli]"
hf download ermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF \
  --include "*q4_k_m*.gguf" --local-dir ./Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF

llama.cpp

# build: https://github.com/ggerganov/llama.cpp
./llama-cli -m ./Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF/granite-4.1-8b-sft-claude-opus-reasoning-unsloth.q2_k.gguf \
  -p "Solve step by step: What is the sum of the first 10 prime numbers?" -n 512

Ollama

ollama run hf.co/ermiaazarkhalili/Granite-4.1-8B-SFT-Claude-Opus-Reasoning-Unsloth-GGUF:Q4_K_M "Solve step by step: What is the sum of the first 10 prime numbers?"

Training Outcome

| Metric | Value |

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

| SLURM Job ID | 38330897 |

| Runtime | 32m 49s |

| Final Training Loss | 0.7895 |

| Peak VRAM | 9.26 GB |

| GPU | H100 80GB HBM3 (MIG 3g.40gb) |

License

APACHE-2.0 — see the base model for full terms.

Acknowledgments

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