Instructions to use Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd with Transformers:

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

pipe = pipeline("text-generation", model="Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd")
model = AutoModelForCausalLM.from_pretrained("Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd

SGLang

How to use Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd 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 "Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd" \
    --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": "Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd",
		"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 "Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd" \
        --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": "Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd with Docker Model Runner:
```
docker model run hf.co/Grogros/Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd
```

Llama-3.2-1B-Instruct-safecoder-distill-3.0-Code-safecoder_distill_reg_full_safecoder_bd

This model was trained from scratch 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: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
optimizer: Use adafactor and the args are: No additional optimizer arguments
lr_scheduler_type: cosine
training_steps: 500

Training results

Framework versions

Transformers 4.51.3
Pytorch 2.2.0a0+81ea7a4
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: 7

Safetensors

Model size

1B params

Tensor type

BF16