Instructions to use Manoghn/tinyllama-lesson-synthesizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Manoghn/tinyllama-lesson-synthesizer with Transformers:

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

pipe = pipeline("text-generation", model="Manoghn/tinyllama-lesson-synthesizer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Manoghn/tinyllama-lesson-synthesizer", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Manoghn/tinyllama-lesson-synthesizer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Manoghn/tinyllama-lesson-synthesizer

SGLang

How to use Manoghn/tinyllama-lesson-synthesizer 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 "Manoghn/tinyllama-lesson-synthesizer" \
    --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": "Manoghn/tinyllama-lesson-synthesizer",
		"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 "Manoghn/tinyllama-lesson-synthesizer" \
        --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": "Manoghn/tinyllama-lesson-synthesizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Manoghn/tinyllama-lesson-synthesizer with Docker Model Runner:
```
docker model run hf.co/Manoghn/tinyllama-lesson-synthesizer
```

Manoghn/tinyllama-lesson-synthesizer

📚 Model Description

This repository hosts Manoghn/tinyllama-lesson-synthesizer, a fine-tuned TinyLlama/TinyLlama-1.1B-Chat-v1.0 model designed to generate comprehensive and engaging educational lessons. It's a key component of the larger SynthAI project, which aims to create multi-modal learning content including lessons, images, quizzes, and audio narration.

The model has been specifically adapted using LoRA (Low-Rank Adaptation) to excel at generating structured, informative text suitable for educational purposes across various domains.

🎯 Objective

The primary objective of this fine-tuned model is to automatically generate detailed educational lessons on diverse topics. By providing a topic, the model produces well-structured, Markdown-formatted content, serving as a foundation for broader educational material synthesis.

📊 Training Data

The model was fine-tuned on a custom-curated dataset of 60 educational lessons.

Data Collection: Lessons were generated using the Llama-3.1-8B-Instruct model via the Hugging Face Inference Client. Each lesson was crafted in response to a detailed prompt instructing the model to act as an "expert educational content creator."
Content Structure: The generated lessons adhered to a specific Markdown format, including:
- A descriptive level-1 heading.
- An introduction explaining the topic's importance.
- 3-5 key concepts with clear explanations.
- Real-world applications or examples.
- Practical examples, formulas, or code snippets (if relevant).
- A concise summary.
Domains Covered: The dataset spans four educational domains:
- Science (e.g., Photosynthesis, Newton's Laws of Motion)
- Mathematics (e.g., Pythagorean Theorem, Quadratic Equations)
- Computer Science (e.g., Binary Number System, Data Structures Overview)
- Humanities (e.g., Renaissance Art Period, World War II Causes)
Dataset Size: The final dataset comprised 60 high-quality lesson examples, split into training (70%), validation (15%), and test (15%) sets.

⚙️ Fine-tuning Methodology

The Manoghn/tinyllama-lesson-synthesizer model was fine-tuned from TinyLlama/TinyLlama-1.1B-Chat-v1.0 using Parameter-Efficient Fine-tuning (PEFT) with LoRA.

Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Quantization: The base model was loaded with 8-bit quantization using BitsAndBytesConfig to reduce memory footprint and enable training on resource-constrained environments (Colab free tier T4 GPU).
LoRA Configuration:
- r=8: LoRA rank
- lora_alpha=32: Scaling factor
- target_modules=["q_proj", "v_proj"]: LoRA adapters applied to query and value projection layers.
- lora_dropout=0.05
- bias="none"
- task_type=TaskType.CAUSAL_LM
Training Parameters (transformers.TrainingArguments):
- output_dir: /content/drive/MyDrive/genai_synthesizer/results
- per_device_train_batch_size=1
- per_device_eval_batch_size=1
- learning_rate=2e-4
- num_train_epochs=1
- logging_steps=10
- fp16=True
- report_to="none"
Training Environment: The fine-tuning was performed on a Google Colab free tier T4 GPU.

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