HF-SmolLM-1.7B-0.5B-4bit-coder

Model Summary

HF-SmolLM-1.7B-0.5B-4bit-coder is a fine-tuned variant of SmolLM-1.7B, optimized for instruction-following in Python code generation tasks.
It was trained on a 1,500-sample subset of the flytech/python-codes-25k dataset using parameter-efficient fine-tuning (QLoRA 4-bit).

The model is suitable for:

• Generating Python code snippets from natural language instructions
• Completing short code functions
• Educational prototyping of fine-tuned LMs

⚠️ This is not a production-ready coding assistant. Generated outputs must be manually reviewed before execution.

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Intended Uses & Limitations

✅ Intended

• Research on parameter-efficient fine-tuning
• Educational demos of instruction-tuning workflows
• Prototype code generation experiments

❌ Not Intended

• Deployment in production coding assistants
• Safety-critical applications
• Long-context multi-file programming tasks

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Training Details

Base Model

• Name: HuggingFaceTB/SmolLM-1.7B
• Architecture: Decoder-only causal LM
• Total Parameters: 1.72B
• Fine-tuned Trainable Parameters: ~9M (0.53%)

Dataset

• Source: flytech/python-codes-25k
• Subset Used: 1,500 randomly sampled examples
• Content: Instruction + optional input → Python code output
• Formatting: Converted into chat format with user / assistant roles

Training Procedure

• Framework: Hugging Face Transformers + TRL (SFTTrainer)
• Quantization: 4-bit QLoRA (nf4) with bfloat16 compute when available
• Effective Batch Size: 6 (with accumulation)
• Optimizer: AdamW
• Scheduler: Cosine decay with warmup ratio 0.05
• Epochs: 3
• Learning Rate: 2e-4
• Max Seq Length: 64 tokens (training)
• Mixed Precision: FP16
• Gradient Checkpointing: Enabled

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Evaluation

No formal benchmark evaluation has been conducted yet.
Empirically, the model:

• Produces syntactically valid Python code for simple tasks
• Adheres to given instructions with reasonable accuracy
• Struggles with multi-step reasoning and long code outputs

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Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "sweatSmile/HF-SmolLM-1.7B-0.5B-4bit-coder"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")

prompt = "Write a Python function that checks if a number is prime."
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    return_tensors="pt",
    add_generation_prompt=True
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))