Instructions to use Haldi247/TinyLlama-SFT-Alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Haldi247/TinyLlama-SFT-Alpaca with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Haldi247/TinyLlama-SFT-Alpaca", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Haldi247/TinyLlama-SFT-Alpaca")
model = AutoModelForCausalLM.from_pretrained("Haldi247/TinyLlama-SFT-Alpaca", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Haldi247/TinyLlama-SFT-Alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Haldi247/TinyLlama-SFT-Alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haldi247/TinyLlama-SFT-Alpaca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Haldi247/TinyLlama-SFT-Alpaca

SGLang

How to use Haldi247/TinyLlama-SFT-Alpaca with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Haldi247/TinyLlama-SFT-Alpaca" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haldi247/TinyLlama-SFT-Alpaca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Haldi247/TinyLlama-SFT-Alpaca" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Haldi247/TinyLlama-SFT-Alpaca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Haldi247/TinyLlama-SFT-Alpaca with Docker Model Runner:
```
docker model run hf.co/Haldi247/TinyLlama-SFT-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

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Safetensors

Model size

1B params

Tensor type

F16

Paper for Haldi247/TinyLlama-SFT-Alpaca

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 59