---
base_model:
  - unsloth/Qwen3-4B
library_name: gguf
pipeline_tag: text-generation
tags:
  - gguf
  - llama.cpp
  - quantized
  - unsloth
  - lora
  - trl
  - sft
---

# Qwen3-4B-SFT-Fable5-Glint-GGUF

GGUF quantizations of a LoRA fine-tune of [`unsloth/Qwen3-4B`](https://huggingface.co/unsloth/Qwen3-4B), supervised fine-tuned on `ermiaazarkhalili/Fable-5-Glint-Clean` (private).

Quantized from [`ermiaazarkhalili/Qwen3-4B-SFT-Fable5-Glint`](https://huggingface.co/ermiaazarkhalili/Qwen3-4B-SFT-Fable5-Glint). See that repository for the full-precision weights.

| | |
| --- | --- |
| **Base model** | [`unsloth/Qwen3-4B`](https://huggingface.co/unsloth/Qwen3-4B) |
| **Training data** | `ermiaazarkhalili/Fable-5-Glint-Clean` (private) |
| **Method** | LoRA supervised fine-tuning via [Unsloth](https://github.com/unslothai/unsloth) + [TRL](https://github.com/huggingface/trl) |

## Available quantizations

| File | Size |
| --- | --- |
| `qwen3-4b-sft-fable5-glint.q4_k_m.gguf` | 2.50 GB |
| `qwen3-4b-sft-fable5-glint.q5_k_m.gguf` | 2.89 GB |
| `qwen3-4b-sft-fable5-glint.q8_0.gguf` | 4.28 GB |

## Usage

### llama.cpp

```bash
huggingface-cli download ermiaazarkhalili/Qwen3-4B-SFT-Fable5-Glint-GGUF qwen3-4b-sft-fable5-glint.q4_k_m.gguf --local-dir .
llama-cli -m qwen3-4b-sft-fable5-glint.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256
```

### Ollama

```bash
echo 'FROM ./qwen3-4b-sft-fable5-glint.q4_k_m.gguf' > Modelfile
ollama create qwen3-4b-sft-fable5-glint-gguf -f Modelfile
ollama run qwen3-4b-sft-fable5-glint-gguf
```

## Training configuration

| Setting | Value |
| --- | --- |
| LoRA rank (r) | 16 |
| LoRA alpha | 16 |
| Learning rate | 0.0002 |
| Epochs | 3 |
| Effective batch size | 8 (2 x 4 grad accum) |
| Max sequence length | 4096 |
| Base precision | 4-bit (QLoRA) |
| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |

## Observed training loss

Measured from our SLURM logs for this configuration. These are training-loss
observations only — no downstream benchmark evaluation has been run on this
model, so they should not be read as a quality claim.

| SLURM job | Steps | First loss | Final loss |
| --- | --- | --- | --- |
| unlabelled | 1,554 | 2.3999 | 0.9811 |

## Limitations

- No benchmark evaluation has been run on this checkpoint. The only reported
  numbers are training-loss observations.
- Inherits the biases, knowledge cutoff and failure modes of the base model.
- Fine-tuned on a single instruction-following dataset; behaviour outside that
  distribution is untested.
- LoRA adapters were merged into the base weights, so the merged model cannot
  be detached from this fine-tune.

## Reproducing

Trained by `notebooks/fable_distillation_qwen3-4b_fable-glint_unsloth.ipynb`, executed non-interactively with
papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).

---

*Card generated from the training run's own configuration and logs by*
*`scripts/generate_hub_model_card.py`.*