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

Libraries

How to use TensorVizion/LLaMaPaca with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "TensorVizion/LLaMaPaca")

Transformers

How to use TensorVizion/LLaMaPaca with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TensorVizion/LLaMaPaca")
model = AutoModelForCausalLM.from_pretrained("TensorVizion/LLaMaPaca", 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 TensorVizion/LLaMaPaca with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TensorVizion/LLaMaPaca

SGLang

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

Unsloth Studio

How to use TensorVizion/LLaMaPaca 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 TensorVizion/LLaMaPaca 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 TensorVizion/LLaMaPaca to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

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

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

LLaMaPaca

Model Details Model Name: LLaMaPaca Base Model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit Adapter Type: LoRA (Low-Rank Adaptation) Library: PEFT (Parameter-Efficient Fine-Tuning) Pipeline Tag: text-generation

Description

LLaMaPaca is a LoRA adapter fine-tuned on the LLaMA 3.2 1B Instruct model using Unsloth's optimized training framework. This adapter enables parameter-efficient customization of the base model for specific tasks or domains while maintaining the core capabilities of LLaMA 3.2. The adapter was trained using 4-bit quantization via bitsandbytes, making it memory-efficient and suitable for deployment on consumer-grade hardware.

Technical Specifications

Architecture: LLaMA 3.2 with LoRA adapters Base Model Size: ~1B parameters Quantization: 4-bit (bitsandbytes) Training Framework: Unsloth + PEFT Adapter Format: PEFT LoRA

Training Details

Method: LoRA (Low-Rank Adaptation) Optimization: Unsloth acceleration Quantization: 4-bit precision with bitsandbytes Framework: PEFT + Transformers Intended Use Cases Instruction following and conversational AI Domain-specific text generation Custom task adaptation with minimal resource requirements Edge deployment scenarios requiring efficient models Limitations Performance depends on the quality and quantity of fine-tuning data May inherit biases from the base LLaMA 3.2 model 4-bit quantization may result in slight accuracy trade-offs Adapter is specific to the base model architecture Citation If you use this model in your research, please cite: bibtex & TensorVizion

License

Please refer to the base model license (LLaMA 3.2 Community License) and specify any additional licensing terms for your adapter.

Downloads last month: 9