Qwen3.5-9B-SFT-Fable5-GGUF

GGUF quantizations of a LoRA fine-tune of unsloth/Qwen3.5-9B, supervised fine-tuned on ermiaazarkhalili/Fable-5-Complete-2M-Clean (private).

Quantized from ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5. See that repository for the full-precision weights.

Base model	unsloth/Qwen3.5-9B
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)

Available quantizations

File	Size
qwen3.5-9b-sft-fable5.q4_k_m.gguf	5.63 GB
qwen3.5-9b-sft-fable5.q5_k_m.gguf	6.47 GB
qwen3.5-9b-sft-fable5.q8_0.gguf	9.53 GB

Usage

llama.cpp

huggingface-cli download ermiaazarkhalili/Qwen3.5-9B-SFT-Fable5-GGUF qwen3.5-9b-sft-fable5.q4_k_m.gguf --local-dir .
llama-cli -m qwen3.5-9b-sft-fable5.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256

Ollama

echo 'FROM ./qwen3.5-9b-sft-fable5.q4_k_m.gguf' > Modelfile
ollama create qwen3.5-9b-sft-fable5-gguf -f Modelfile
ollama run qwen3.5-9b-sft-fable5-gguf

Training configuration

Setting	Value
LoRA rank (r)	16
LoRA alpha	16
Learning rate	0.0002
Epochs	1
Effective batch size	8 (1 x 8 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, out_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	47,127	0.9168	0.8932

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_qwen35-9b_fable_unsloth.ipynb, executed non-interactively with papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).

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