Instructions to use Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST with Transformers:

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

pipe = pipeline("text-generation", model="Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST")
model = AutoModelForCausalLM.from_pretrained("Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST

SGLang

How to use Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST 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 "Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST" \
    --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": "Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST",
		"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 "Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST" \
        --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": "Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST with Docker Model Runner:
```
docker model run hf.co/Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST
```

Llama3.2-3B-Breadcrumbs-Base-INST

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Breadcrumbs merge method using meta-llama/Llama-3.2-3B as a base.

Models Merged

The following models were included in the merge:

meta-llama/Llama-3.2-3B-Instruct

Configuration

The following YAML configuration was used to produce this model:

base_model: meta-llama/Llama-3.2-3B
dtype: float16
merge_method: breadcrumbs
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 26]
        model: meta-llama/Llama-3.2-3B-Instruct
        parameters:
          weight: 0.8
      - layer_range: [0, 26]
        model: meta-llama/Llama-3.2-3B
parameters:
  density: 0.9
  gamma: 0.01
  normalize: 1.0

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Safetensors

Model size

3B params

Tensor type

F16

Model tree for Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST

meta-llama/Llama-3.2-3B

meta-llama/Llama-3.2-3B-Instruct

Merge model

this model

Paper for Alelcv27/Llama3.2-3B-Breadcrumbs-Base-INST

Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks

Paper • 2312.06795 • Published Dec 11, 2023 • 2