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

Libraries

How to use ConnectorLogic/LogicConnector-Llama-3.2-3B with Transformers:

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

pipe = pipeline("text-generation", model="ConnectorLogic/LogicConnector-Llama-3.2-3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ConnectorLogic/LogicConnector-Llama-3.2-3B")
model = AutoModelForCausalLM.from_pretrained("ConnectorLogic/LogicConnector-Llama-3.2-3B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ConnectorLogic/LogicConnector-Llama-3.2-3B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ConnectorLogic/LogicConnector-Llama-3.2-3B

SGLang

How to use ConnectorLogic/LogicConnector-Llama-3.2-3B 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 "ConnectorLogic/LogicConnector-Llama-3.2-3B" \
    --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": "ConnectorLogic/LogicConnector-Llama-3.2-3B",
		"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 "ConnectorLogic/LogicConnector-Llama-3.2-3B" \
        --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": "ConnectorLogic/LogicConnector-Llama-3.2-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ConnectorLogic/LogicConnector-Llama-3.2-3B with Docker Model Runner:
```
docker model run hf.co/ConnectorLogic/LogicConnector-Llama-3.2-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LogicConnector-Llama-3.2-3B

This model is a fine-tuned version of meta-llama/Llama-3.2-3B on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 1

Training results

Framework versions

Transformers 5.7.0
Pytorch 2.11.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2

Downloads last month: 9

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for ConnectorLogic/LogicConnector-Llama-3.2-3B

Base model

meta-llama/Llama-3.2-3B

Finetuned

(492)

this model

Quantizations

1 model