Instructions to use EmTpro01/CodeLlama-7b-java-16bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmTpro01/CodeLlama-7b-java-16bit with Transformers:

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

pipe = pipeline("text-generation", model="EmTpro01/CodeLlama-7b-java-16bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EmTpro01/CodeLlama-7b-java-16bit")
model = AutoModelForCausalLM.from_pretrained("EmTpro01/CodeLlama-7b-java-16bit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EmTpro01/CodeLlama-7b-java-16bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmTpro01/CodeLlama-7b-java-16bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmTpro01/CodeLlama-7b-java-16bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EmTpro01/CodeLlama-7b-java-16bit

SGLang

How to use EmTpro01/CodeLlama-7b-java-16bit 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 "EmTpro01/CodeLlama-7b-java-16bit" \
    --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": "EmTpro01/CodeLlama-7b-java-16bit",
		"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 "EmTpro01/CodeLlama-7b-java-16bit" \
        --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": "EmTpro01/CodeLlama-7b-java-16bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use EmTpro01/CodeLlama-7b-java-16bit with Docker Model Runner:
```
docker model run hf.co/EmTpro01/CodeLlama-7b-java-16bit
```

CodeLlama-7b-java-16bit / pytorch_model-00002-of-00003.bin

EmTpro01

Trained with Unsloth

90d1a79 verified almost 2 years ago

History Blame

4.95 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: aa0a7c3d620fc187df94153df05a14aa0313bb1f312a0c140704545c78b77d1a
Size of remote file:: 4.95 GB
SHA256:: 8b3546ffc1adb9a625bbde245c40088bb2729e718c8f3b67bdb2ffef9752a6c3

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

EmTpro01
/

CodeLlama-7b-java-16bit

Detected Pickle imports (3)

Xet Pointer Details