chatOP — SmolLM2-135M Fine-tuned

A fine-tuned version of SmolLM2-135M-Instruct trained to act as a friendly study assistant for CS and ML concepts.

Model Details

Base model	HuggingFaceTB/SmolLM2-135M-Instruct
Model type	Causal Language Model
Fine-tuning method	SFT + LoRA
Language	English
License	Apache 2.0

Training Details

Dataset

• Name: HuggingFaceTB/smoltalk
• Split used: all
• Format: Conversational chat messages (system, user, assistant)

Hyperparameters

Parameter	Value
Learning rate	2e-4
Training steps	200
Batch size	2
Packing	True
Optimizer	AdamW
LR scheduler	Linear
Precision	fp16

LoRA Config

Parameter	Value
Rank (r)	16
Alpha (lora_alpha)	16
Dropout	0.05
Target modules	all-linear
Bias	none
Trainable parameters	~1.7M / 135M (1.26%)

Hardware

• GPU: NVIDIA Tesla T4 (Google Colab)
• Training time: ~15–20 minutes

Example Outputs

Prompt: What is machine learning?

Machine learning is a field of AI where systems learn from data to make predictions or decisions without being explicitly programmed for each task.

Prompt: Explain transformers in simple terms.

A Transformer is a neural network architecture that uses attention mechanisms to understand relationships between all words in a sentence simultaneously, rather than reading them one by one like older models did.

Prompt: What is the difference between LoRA and full fine-tuning?

Full fine-tuning updates all parameters of a model which requires a lot of memory. LoRA instead freezes the base model and adds small trainable matrices to each layer, training only ~1% of parameters while achieving similar results.

Limitations

• Small model (135M parameters) — responses may be less detailed than larger models
• Trained for only 200 steps — longer training would improve quality
• Best for short explanations — may struggle with very complex multi-step reasoning
• Not suitable for factual lookups — may hallucinate specific facts or numbers

Training Framework

Built as a learning project while studying the HuggingFace LLM Course — specifically Chapter 11 (Fine-tuning LLMs).

Libraries used:

• 🤗 Transformers
• 🤗 PEFT
• TRL (SFTTrainer)
• 🤗 Datasets
• Accelerate

Author

Made by puravky — undergrad student exploring ML and AI.