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ermiaazarkhalili/Gemma4-E4B-SFT-Fable5-GGUF
Gemma4-E4B-SFT-Fable5-GGUF is a text generation model from ermiaazarkhalili. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as apache-2.0.
GGUF quantizations of a LoRA fine-tune of unsloth/gemma-4-E4B-it, supervised fine-tuned on ermiaazarkhalili/Fable-5-Complete-2M-Clean (private).
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From the Hugging Face model README
GGUF quantizations of a LoRA fine-tune of unsloth/gemma-4-E4B-it, supervised fine-tuned on ermiaazarkhalili/Fable-5-Complete-2M-Clean (private).
Quantized from ermiaazarkhalili/Gemma4-E4B-SFT-Fable5. See that repository for the full-precision weights.
| Base model | unsloth/gemma-4-E4B-it |
| Training data | ermiaazarkhalili/Fable-5-Complete-2M-Clean (private) |
| Method | LoRA supervised fine-tuning via Unsloth + TRL |
| License | apache-2.0 (inherited from the base model) |
| File | Size |
|---|---|
gemma4-e4b-sft-fable5.q4_k_m.gguf | 5.34 GB |
gemma4-e4b-sft-fable5.q5_k_m.gguf | 5.76 GB |
gemma4-e4b-sft-fable5.q8_0.gguf | 8.03 GB |
huggingface-cli download ermiaazarkhalili/Gemma4-E4B-SFT-Fable5-GGUF gemma4-e4b-sft-fable5.q4_k_m.gguf --local-dir .
llama-cli -m gemma4-e4b-sft-fable5.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256
echo 'FROM ./gemma4-e4b-sft-fable5.q4_k_m.gguf' > Modelfile
ollama create gemma4-e4b-sft-fable5-gguf -f Modelfile
ollama run gemma4-e4b-sft-fable5-gguf
| Setting | Value |
|---|---|
| LoRA rank (r) | 16 |
| LoRA alpha | 16 |
| Learning rate | 0.0002 |
| Epochs | 2 |
| Effective batch size | 8 (2 x 4 grad accum) |
| Max sequence length | 4096 |
| Base precision | 4-bit (QLoRA) |
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 |
|---|---|---|---|
53221041 | 94,252 | 2.7514 | 0.9215 |
Trained by notebooks/fable_distillation_gemma4-e4b_fable_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.