Instructions to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf",
	filename="llama_3.2_1b-lora-qlora-dart-llm_q5_k_m.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M

Use Docker

docker model run hf.co/YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M

LM Studio
Jan

vLLM

How to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M

Ollama
How to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf with Ollama:
```
ollama run hf.co/YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M
```

Unsloth Studio

How to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf 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 YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf 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 YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf with Docker Model Runner:
```
docker model run hf.co/YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M
```

Lemonade

How to use YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf:Q5_K_M

Run and chat with the model

lemonade run user.llama-3.2-1b-lora-qlora-dart-llm-gguf-Q5_K_M

List all available models

lemonade list

Llama 3.2 1B DART LLM - GGUF Quantized Models

This repository contains GGUF quantized versions of the Llama 3.2 1B DART LLM model, fine-tuned for robot task planning in construction environments.

Model Details

Base Model: meta-llama/Llama-3.2-1B
Fine-tuned Version: Based on QLoRA fine-tuned model for robotics task planning
Format: GGUF (GPT-Generated Unified Format)
Use Case: Optimized for inference with llama.cpp and compatible frameworks

Available Files

M: llama_3.2_1b-lora-qlora-dart-llm_q5_k_m.gguf - m quantization

Usage with llama.cpp

# Clone llama.cpp repository
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# Build llama.cpp
make

# Download a quantized model (example with q4_k_m)
wget https://huggingface.co/YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf/resolve/main/{model_filename}_q4_k_m.gguf

# Run inference
./main -m {model_filename}_q4_k_m.gguf -p "### Instruction:\nDeploy Excavator 1 to Soil Area 1 for excavation\n\n### Response:\n" -n 512

Usage with Python (llama-cpp-python)

from llama_cpp import Llama

# Load model
llm = Llama(model_path="{model_filename}_q4_k_m.gguf", n_ctx=2048)

# Generate response
prompt = "### Instruction:\nDeploy Excavator 1 to Soil Area 1 for excavation\n\n### Response:\n"
output = llm(prompt, max_tokens=512, stop=["</s>"], echo=False)

print(output['choices'][0]['text'])

Quantization Details

Different quantization levels offer trade-offs between model size, inference speed, and quality:

f16: Full 16-bit precision (largest, highest quality)
q8_0: 8-bit quantization (good balance of size and quality)
q5_k_m: 5-bit quantization with mixed precision (recommended)
q4_k_m: 4-bit quantization (good for most use cases)
q3_k_m: 3-bit quantization (smaller, some quality loss)
q2_k: 2-bit quantization (smallest, significant quality loss)

Performance

The model generates structured JSON task sequences for construction robotics:

{
  "tasks": [
    {
      "instruction_function": {
        "dependencies": [],
        "name": "target_area_for_specific_robots", 
        "object_keywords": ["soil_area_1"],
        "robot_ids": ["robot_excavator_01"],
        "robot_type": null
      },
      "task": "target_area_for_specific_robots_1"
    }
  ]
}

Original Model

This GGUF model is converted from: YongdongWang/llama-3.2-1b-lora-qlora-dart-llm

License

This model inherits the license from the base model (meta-llama/Llama-3.2-1B).

Citation

@misc{llama_3.2_1b_lora_qlora_dart_llm_gguf,
  title={Llama 3.2 1B DART LLM - GGUF Quantized Models},
  author={YongdongWang},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf}
}

Downloads last month: 6

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

5-bit

Model tree for YongdongWang/llama-3.2-1b-lora-qlora-dart-llm-gguf

Base model

meta-llama/Llama-3.2-1B

Quantized

(243)

this model

YongdongWang
/

llama-3.2-1b-lora-qlora-dart-llm-gguf