Instructions to use MathBite/llama1b_finetuned_json_creation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MathBite/llama1b_finetuned_json_creation with Transformers:

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

pipe = pipeline("text-generation", model="MathBite/llama1b_finetuned_json_creation")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MathBite/llama1b_finetuned_json_creation", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MathBite/llama1b_finetuned_json_creation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MathBite/llama1b_finetuned_json_creation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MathBite/llama1b_finetuned_json_creation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MathBite/llama1b_finetuned_json_creation

SGLang

How to use MathBite/llama1b_finetuned_json_creation 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 "MathBite/llama1b_finetuned_json_creation" \
    --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": "MathBite/llama1b_finetuned_json_creation",
		"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 "MathBite/llama1b_finetuned_json_creation" \
        --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": "MathBite/llama1b_finetuned_json_creation",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use MathBite/llama1b_finetuned_json_creation with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MathBite/llama1b_finetuned_json_creation to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MathBite/llama1b_finetuned_json_creation to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MathBite/llama1b_finetuned_json_creation to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="MathBite/llama1b_finetuned_json_creation",
    max_seq_length=2048,
)

Docker Model Runner
How to use MathBite/llama1b_finetuned_json_creation with Docker Model Runner:
```
docker model run hf.co/MathBite/llama1b_finetuned_json_creation
```

Llama 3.2 1B JSON Extractor

A fine-tuned version of Llama 3.2 1B Instruct specialized for generating structured JSON outputs with high accuracy and schema compliance.

🎯 Model Description

This model has been fine-tuned to excel at generating valid, well-structured JSON objects based on Pydantic model schemas. It transforms natural language prompts into properly formatted JSON responses with remarkable consistency.

📊 Performance

🚀 Dramatic Improvement in JSON Generation:

JSON Validity Rate: 20% → 92% (over 70% improvement)
Schema Compliance: Near-perfect adherence to small-average size Pydantic model structures
Generalization: Successfully handles completely new, unseen Pydantic model classes

🔧 Training Details

Base Model: meta-llama/Llama-3.2-1B-Instruct
Fine-tuning Method: LoRA (Low-Rank Adaptation) with Unsloth
Training Data: Synthetic dataset with 15+ diverse Pydantic model types
Training Epochs: 15
Batch Size: 16 (with gradient accumulation)
Learning Rate: 1e-4

🏗️ Supported Model Types

The model can generate JSON for 15+ different object types including:

Educational: Course, Resume, Events
Entertainment: FilmIdea, BookReview, GameIdea
Business: TShirtOrder, Recipe, House
Characters & Gaming: FictionalCharacter, GameArtifact
Travel: Itinerary
Science: SollarSystem, TextSummary
And many more...

🎯 Key Features

High JSON Validity: 92% success rate in generating valid JSON
Schema Compliance: Follows Pydantic model structures precisely
Strong Generalization: Works with new, unseen model classes
Consistent Output: Reliable structured data generation
Lightweight: Only 1B parameters for efficient deployment

📚 Training Data

The model was fine-tuned on a synthetic dataset containing thousands of examples across diverse domains:

Character creation and game development
Business and e-commerce objects
Educational and professional content
Entertainment and media descriptions
Scientific and technical data structures

🔗 Links

GitHub Repository: LLM_FineTuning_4JsonCreation
Base Model: meta-llama/Llama-3.2-1B-Instruct

📄 License

This model is released under the Apache 2.0 license.

🙏 Acknowledgments

Meta for the base Llama 3.2 model
Unsloth for efficient fine-tuning framework
Hugging Face for model hosting and ecosystem

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Model tree for MathBite/llama1b_finetuned_json_creation

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

(1728)

this model