| --- |
| language: |
| - en |
| license: apache-2.0 |
| base_model: HuggingFaceTB/SmolLM2-135M-Instruct |
| tags: |
| - llm |
| - fine-tuned |
| - lora |
| - sft |
| - text-generation |
| - student-project |
| datasets: |
| - HuggingFaceTB/smoltalk |
| pipeline_tag: text-generation |
| --- |
| |
| # chatOP — SmolLM2-135M Fine-tuned |
|
|
| A fine-tuned version of [SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/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](https://huggingface.co/datasets/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](https://huggingface.co/learn/llm-course) — specifically Chapter 11 (Fine-tuning LLMs). |
|
|
| **Libraries used:** |
| - 🤗 Transformers |
| - 🤗 PEFT |
| - TRL (SFTTrainer) |
| - 🤗 Datasets |
| - Accelerate |
|
|
| ## Author |
|
|
| Made by [puravky](https://huggingface.co/puravky) — undergrad student exploring ML and AI. |
|
|
