Granite-4.1-3B-SFT-Fable5-Glint
A LoRA fine-tune of ibm-granite/granite-4.1-3b, supervised fine-tuned on ermiaazarkhalili/Fable-5-Glint-Clean (private).
| Base model | ibm-granite/granite-4.1-3b |
| Architecture | GraniteForCausalLM |
| Parameters | 3.4B |
| 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-4.1-3B-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-4.1-3b. Assistant tokens only; both models are scored identically.
| Metric | Base | This model | Δ |
|---|---|---|---|
| Top-1 accuracy | 0.5309 | 0.7027 | +0.1718 |
| Top-5 accuracy | 0.7950 | 0.9153 | +0.1203 |
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 |
|---|---|---|---|
46020983 | 1,557 | 1.3467 | 0.7531 |
45987984 | 1,050 | 1.4098 | 0.9077 |
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_granite41-3b_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.