101 lines
2.9 KiB
Markdown
101 lines
2.9 KiB
Markdown
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---
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language:
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- en
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license: apache-2.0
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base_model: HuggingFaceTB/SmolLM2-135M-Instruct
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tags:
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- llm
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- fine-tuned
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- lora
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- sft
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- text-generation
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- student-project
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datasets:
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- HuggingFaceTB/smoltalk
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pipeline_tag: text-generation
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---
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# chatOP — SmolLM2-135M Fine-tuned
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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.
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## Model Details
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|---|---|
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| **Base model** | HuggingFaceTB/SmolLM2-135M-Instruct |
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| **Model type** | Causal Language Model |
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| **Fine-tuning method** | SFT + LoRA |
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| **Language** | English |
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| **License** | Apache 2.0 |
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## Training Details
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### Dataset
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- **Name:** [HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)
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- **Split used:** `all`
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- **Format:** Conversational chat messages (system, user, assistant)
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### Hyperparameters
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| Parameter | Value |
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|---|---|
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| Learning rate | 2e-4 |
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| Training steps | 200 |
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| Batch size | 2 |
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| Packing | True |
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| Optimizer | AdamW |
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| LR scheduler | Linear |
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| Precision | fp16 |
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### LoRA Config
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| Parameter | Value |
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|---|---|
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| Rank (`r`) | 16 |
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| Alpha (`lora_alpha`) | 16 |
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| Dropout | 0.05 |
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| Target modules | all-linear |
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| Bias | none |
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| Trainable parameters | ~1.7M / 135M (1.26%) |
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### Hardware
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- **GPU:** NVIDIA Tesla T4 (Google Colab)
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- **Training time:** ~15–20 minutes
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## Example Outputs
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**Prompt:** What is machine learning?
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> Machine learning is a field of AI where systems learn from data to make predictions or decisions without being explicitly programmed for each task.
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**Prompt:** Explain transformers in simple terms.
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> 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.
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**Prompt:** What is the difference between LoRA and full fine-tuning?
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> 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.
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## Limitations
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- Small model (135M parameters) — responses may be less detailed than larger models
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- Trained for only 200 steps — longer training would improve quality
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- Best for short explanations — may struggle with very complex multi-step reasoning
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- Not suitable for factual lookups — may hallucinate specific facts or numbers
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## Training Framework
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Built as a learning project while studying the [HuggingFace LLM Course](https://huggingface.co/learn/llm-course) — specifically Chapter 11 (Fine-tuning LLMs).
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**Libraries used:**
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- 🤗 Transformers
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- 🤗 PEFT
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- TRL (SFTTrainer)
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- 🤗 Datasets
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- Accelerate
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## Author
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Made by [puravky](https://huggingface.co/puravky) — undergrad student exploring ML and AI.
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