granite-claude-h-350m
A reasoning-focused fine-tune of IBM Granite 4.0 H 350M.
This model was trained on the
Avtrkrb/combined-reasoning-claude
dataset using Unsloth and LoRA fine-tuning.
The objective of the fine-tuning process was to improve:
- Multi-step reasoning
- Instruction following
- Logical problem solving
- Structured thought processes
- Explicit reasoning traces
Base Model
This model is derived from:
unsloth/granite-4.0-h-350m-unsloth-bnb-4bit
which itself is an Unsloth-optimized version of IBM Granite 4.0 H 350M.
Dataset
Training dataset:
https://huggingface.co/datasets/Avtrkrb/combined-reasoning-claude
The dataset contains instruction/response pairs focused on reasoning tasks.
Intended Use
This model is intended for:
- Reasoning assistants
- Educational applications
- Small local inference deployments
- Research experiments
- Lightweight reasoning workloads
Limitations
This model is only 350M parameters.
Users should expect:
- Reduced factual accuracy compared to larger models
- Occasional reasoning failures
- Hallucinations
- Limited world knowledge
Outputs should be verified before use in high-stakes settings.
Example Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Avtrkrb/granite-claude-h-350m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
prompt = "Explain why the sky appears blue."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=512,
)
print(tokenizer.decode(outputs[0]))
Training
Fine-tuned using:
- Unsloth
- LoRA
- PEFT
- Transformers
Acknowledgements
- IBM Granite Team
- Unsloth
- Hugging Face
License
This repository follows the license of the underlying Granite model.