Granite-4.1-8B-SFT-Fable5-Glint/README.md

license, base_model, library_name, pipeline_tag, tags

license	base_model	library_name	pipeline_tag	tags
apache-2.0	ibm-granite/granite-4.1-8b	transformers	text-generation	unsloth lora trl sft

Granite-4.1-8B-SFT-Fable5-Glint

A LoRA fine-tune of ibm-granite/granite-4.1-8b, supervised fine-tuned on ermiaazarkhalili/Fable-5-Glint-Clean (private).

Base model	ibm-granite/granite-4.1-8b
Architecture	GraniteForCausalLM
Parameters	8.8B
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)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ermiaazarkhalili/Granite-4.1-8B-SFT-Fable5-Glint"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype='auto', device_map='auto')

messages = [{"role": "user", "content": "Explain gradient checkpointing in two sentences."}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors='pt'
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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

Held-out evaluation

Next-token accuracy on a deterministic held-out split of ermiaazarkhalili/Fable-5-Glint-Clean (n = 199 samples), scored against the base model ibm-granite/granite-4.1-8b. Assistant tokens only; both models are scored identically.

Metric	Base	This model	Δ
Top-1 accuracy	0.5502	0.7258	+0.1756
Top-5 accuracy	0.8136	0.9305	+0.1169

A delta measures how far fine-tuning moved this model from its own starting point; it is not a ranking against other models, which start from different baselines.

Observed training loss

Measured from our SLURM logs for this configuration. These are training-loss observations only — see the held-out evaluation above for measured accuracy.

SLURM job	Steps	First loss	Final loss
46020984	1,557	1.2249	0.6531
45987985	900	1.3237	0.8099

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_granite41-8b_fable-glint_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.