from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Rzkoohi/Qwen-2.5-0.5B-gsm8k"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

messages = [
    {
        "role": "user",
        "content": "If John has 5 apples and buys 3 more, then gives away 2, how many apples does he have?"
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=256
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

Recommended for:

Mathematical reasoning
Educational experiments
Fine-tuning demonstrations
Small-model research
Learning QLoRA workflows

Not recommended for:

Production deployments
Complex reasoning
Long-context tasks
High-accuracy mathematical applications

Limitations

This model has only been fine-tuned on GSM8K.

Although mathematical reasoning is improved, performance on unrelated tasks may differ from the original instruction model. Like all 0.5B parameter models, it has limited world knowledge and reasoning capacity compared to larger LLMs.

Future Work

Planned improvements include:

Evaluation against the original base model
Additional reasoning datasets
Multi-domain instruction tuning
GGUF and merged model releases
Benchmark reporting

Rzkoohi/
Qwen-2.5-0.5B-gsm8k

Model Details

Training

Usage

Intended Use

Limitations

Future Work

Acknowledgements

Citation