Instructions to use vhab10/Llama-3.2-Instruct-3B-TIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vhab10/Llama-3.2-Instruct-3B-TIES with Transformers:

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

pipe = pipeline("text-generation", model="vhab10/Llama-3.2-Instruct-3B-TIES")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vhab10/Llama-3.2-Instruct-3B-TIES")
model = AutoModelForCausalLM.from_pretrained("vhab10/Llama-3.2-Instruct-3B-TIES", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vhab10/Llama-3.2-Instruct-3B-TIES with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vhab10/Llama-3.2-Instruct-3B-TIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vhab10/Llama-3.2-Instruct-3B-TIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vhab10/Llama-3.2-Instruct-3B-TIES

SGLang

How to use vhab10/Llama-3.2-Instruct-3B-TIES 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 "vhab10/Llama-3.2-Instruct-3B-TIES" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vhab10/Llama-3.2-Instruct-3B-TIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "vhab10/Llama-3.2-Instruct-3B-TIES" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vhab10/Llama-3.2-Instruct-3B-TIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use vhab10/Llama-3.2-Instruct-3B-TIES with Docker Model Runner:
```
docker model run hf.co/vhab10/Llama-3.2-Instruct-3B-TIES
```

Llama-3.2-Instruct-3B-TIES

Overview

The Llama-3.2-Instruct-3B-TIES model is a result of merging three versions of Llama-3.2-3B models using the TIES merging method, facilitated by mergekit. This merge combines a base general-purpose language model with two instruction-tuned models to create a more powerful and versatile model capable of handling diverse tasks.

Model Details

Model Description

Models Used:
- meta-llama/Llama-3.2-3B (Base Model)
- meta-llama/Llama-3.2-3B-Instruct
- unsloth/Llama-3.2-3B-Instruct
Merging Tool: Mergekit
Merge Method: TIES
Data Type: float16 (FP16) precision
License: MIT License

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: meta-llama/Llama-3.2-3B
    # Base model
  - model: meta-llama/Llama-3.2-3B-Instruct
    parameters:
      density: 0.5
      weight: 0.5
  - model: unsloth/Llama-3.2-3B-Instruct
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: meta-llama/Llama-3.2-3B
parameters:
  normalize: true
dtype: float16

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Safetensors

Model size

3B params

Tensor type

F32

F16

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