Instructions to use hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp with Transformers:

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

pipe = pipeline("text-generation", model="hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp

SGLang

How to use hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp 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 "hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp" \
    --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": "hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp",
		"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 "hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp" \
        --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": "hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp with Docker Model Runner:
```
docker model run hf.co/hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp
```

CodeLlama-7b-hf-finetuned-py-to-cpp

This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the XLCoST (Python-C++) dataset, restricted to code snippets of <= 128 tokens long. It achieves the following results on the evaluation set:

Loss: 0.3878

Test set:

BLEU: 65.06
COMET: 89.13
CodeBLEU: 78.52
- N-gram match score: 66.81
- Weighted n-gram match score: 82.49
- Syntax match score: 75.77
- Dataflow match score: 89.02

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: 0.0001
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.99	67	0.8366
No log	2.0	135	0.4170
No log	2.98	201	0.3878

Framework versions

Transformers 4.33.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.13.3

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Model tree for hugo-albert/CodeLlama-7b-hf-finetuned-py-to-cpp

Base model

codellama/CodeLlama-7b-hf

Finetuned

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