
Base model	`empero-ai/Qwen3.8-2B`
Training data	`ermiaazarkhalili/Fable-5-Glint-Clean` (private)
Method	LoRA supervised fine-tuning via Unsloth + TRL
License	`apache-2.0` (inherited from the base model)

Available quantizations

File	Size
`qwen3.8-2b-sft-fable5-glint.q2_k.gguf`	990 MB
`qwen3.8-2b-sft-fable5-glint.q3_k_m.gguf`	1.13 GB
`qwen3.8-2b-sft-fable5-glint.q4_k_m.gguf`	1.31 GB
`qwen3.8-2b-sft-fable5-glint.q5_k_m.gguf`	1.45 GB
`qwen3.8-2b-sft-fable5-glint.q6_k.gguf`	1.61 GB
`qwen3.8-2b-sft-fable5-glint.q8_0.gguf`	2.08 GB

Usage

llama.cpp

bash

huggingface-cli download ermiaazarkhalili/Qwen3.8-2B-SFT-Fable5-Glint-GGUF qwen3.8-2b-sft-fable5-glint.q4_k_m.gguf --local-dir .
llama-cli -m qwen3.8-2b-sft-fable5-glint.q4_k_m.gguf -p "Explain gradient checkpointing in two sentences." -n 256

Ollama

bash

echo 'FROM ./qwen3.8-2b-sft-fable5-glint.q4_k_m.gguf' > Modelfile
ollama create qwen3.8-2b-sft-fable5-glint-gguf -f Modelfile
ollama run qwen3.8-2b-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 (1 x 8 grad accum)
Max sequence length	4096
Base precision	4-bit (QLoRA)
Target modules	`down_proj`, `gate_proj`, `in_proj_qkv`, `in_proj_z`, `k_proj`, `o_proj`, `out_proj`, `q_proj`, `up_proj`, `v_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
`55541064`	1,554	1.2842	0.8579

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_qwen38-2b_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.