Instructions to use JetBrains/CodeLlama-7B-Kexer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetBrains/CodeLlama-7B-Kexer with Transformers:

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

pipe = pipeline("text-generation", model="JetBrains/CodeLlama-7B-Kexer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains/CodeLlama-7B-Kexer")
model = AutoModelForCausalLM.from_pretrained("JetBrains/CodeLlama-7B-Kexer")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JetBrains/CodeLlama-7B-Kexer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JetBrains/CodeLlama-7B-Kexer

SGLang

How to use JetBrains/CodeLlama-7B-Kexer 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 "JetBrains/CodeLlama-7B-Kexer" \
    --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": "JetBrains/CodeLlama-7B-Kexer",
		"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 "JetBrains/CodeLlama-7B-Kexer" \
        --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": "JetBrains/CodeLlama-7B-Kexer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JetBrains/CodeLlama-7B-Kexer with Docker Model Runner:
```
docker model run hf.co/JetBrains/CodeLlama-7B-Kexer
```

Titovs commited on May 22, 2024

Commit

78c8aad

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@@ -69,11 +69,11 @@ The model was trained on one A100 GPU with the following hyperparameters:
 |        `total_batch_size`        |          256 (~130K tokens per step)          |
 |        `num_epochs`        |          4          |
-More details about fine-tuning can be found in the technical report.
 # Fine-tuning data
-For tuning this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices dataset](https://huggingface.co/datasets/JetBrains/KExercises). Every example follows the HumanEval format. In total, the dataset contains about 3.5M tokens.
 # Evaluation

 |        `total_batch_size`        |          256 (~130K tokens per step)          |
 |        `num_epochs`        |          4          |
+More details about fine-tuning can be found in the technical report (coming soon).
 # Fine-tuning data
+For tuning this model, we used 15K exmaples from the synthetically generated [Kotlin Exercices](https://huggingface.co/datasets/JetBrains/KExercises) dataset. Every example follows the HumanEval format. In total, the dataset contains about 3.5M tokens.
 # Evaluation