
Base model	`unsloth/Qwen3.5-0.8B`
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
`qwen3.5-0.8b-sft-fable5.q4_k_m.gguf`	529 MB
`qwen3.5-0.8b-sft-fable5.q5_k_m.gguf`	578 MB
`qwen3.5-0.8b-sft-fable5.q8_0.gguf`	812 MB

Usage

llama.cpp

bash

huggingface-cli download ermiaazarkhalili/Qwen3.5-0.8B-SFT-Fable5-GGUF qwen3.5-0.8b-sft-fable5.q4_k_m.gguf --local-dir .
llama-cli -m qwen3.5-0.8b-sft-fable5.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256

Ollama

bash

echo 'FROM ./qwen3.5-0.8b-sft-fable5.q4_k_m.gguf' > Modelfile
ollama create qwen3.5-0.8b-sft-fable5-gguf -f Modelfile
ollama run qwen3.5-0.8b-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	1.4207	1.1952
`45987260`	25,800	1.4193	1.2380

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_qwen35-0.8b_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.