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Model: Haldi247/TinyLlama-SFT-Alpaca
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- instruction-tuning
+- sft
+- lora
+- tinyllama
+- alpaca
+---
+
+# TinyLlama-SFT-Alpaca
+
+TinyLlama-1.1B-Chat fine-tuned using Supervised Fine-Tuning (SFT) with LoRA on the yahma/alpaca-cleaned dataset as part of IBA Karachi NLP with Deep Learning Assignment 4.
+
+## Model Details
+
+- **Developed by:** Hadeeqa Al Islam
+- **Model type:** Causal Language Model
+- **Language:** English
+- **Finetuned from:** TinyLlama/TinyLlama-1.1B-Chat-v1.0
+- **Training data:** yahma/alpaca-cleaned (20,000 samples)
+- **LoRA config:** r=8, lora_alpha=16, target_modules=[q_proj, v_proj]
+- **Training:** lr=2e-4, batch=4, epochs=2, fp16=True
+
+## How to Get Started
+
+tokenizer = AutoTokenizer.from_pretrained("Haldi247/TinyLlama-SFT-Alpaca")
+model = AutoModelForCausalLM.from_pretrained("Haldi247/TinyLlama-SFT-Alpaca")
+messages = [{"role": "user", "content": "What is photosynthesis?"}]
+
+## Training Details
+
+- **Dataset:** yahma/alpaca-cleaned (20,000 samples after filtering)
+- **Hardware:** NVIDIA RTX 5070 Ti (16GB VRAM) via WSL2
+- **Training time:** ~14 minutes
+
+### Training Procedure
+
+#### Training Hyperparameters
+- **Training regime:** fp16 mixed precision
+- **Learning Rate:** 2e-4
+- **Batch Size:** 4
+- **Epochs:** 2
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Metrics
+- **BLEU Score**: Used to evaluate the overlap of n-grams between the model output and the ground truth.
+- **BERTScore**: Used to compute semantic similarity between the generated text and reference text using BERT embeddings.
+
+### Results
+- **Average BLEU Score**: 0.4303
+- **Average BERTScore**: 0.7236
+
+## Limitations
+- **Tokenization Constraints**: The training process used sequence packing to manage memory effectively. Because the model was trained with packed sequences without special tokens (to prevent cross-contamination), it may exhibit formatting issues if the inference environment does not strictly adhere to the expected chat template.
+- **Data Bias**: The model is fine-tuned on the Alpaca dataset, which may inherit biases present in the synthetic instruction data.
+- **Inference Stability**: Due to the aggressive packing strategy, users may observe repetition or formatting artifacts if the prompt structure deviates significantly from the training format.
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
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+
+### Compute Infrastructure
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+#### Hardware
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+
+#### Software
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+
+## Citation [optional]
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+## Glossary [optional]
+
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+## More Information [optional]
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