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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetBrains/CodeLlama-7B-KStack"
# 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-KStack",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

Titovs

Aspr commited on May 16, 2024

Commit

7a39883

verified ·

1 Parent(s): 4a2951c

Update README.md (#3)

Browse files

- Update README.md (4cb75db75e271b9bd2dea8da324f4293687bf169)

Co-authored-by: Mikhail Evtikhiev <Aspr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -86,15 +86,15 @@ We clean the content of the remaining dataset entries according to the following
 # Evaluation
-To evaluate we used Kotlin Humaneval (more infromation here)
 Fine-tuned model:
 |         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
 |:---------------------------:|:----------------------------------------:|
 |           `base model`            |           26.09            |
-|        `fine-tuned model`        |          29.19         |
 # Ethical Considerations and Limitations
-Code Llama and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, Kexer's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of Kexer, developers should perform safety testing and tuning tailored to their specific applications of the model.

 # Evaluation
+To evaluate we used [Kotlin Humaneval](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval)
 Fine-tuned model:
 |         **Model name**           |             **Kotlin HumanEval Pass Rate**              |
 |:---------------------------:|:----------------------------------------:|
 |           `base model`            |           26.09            |
+|        `fine-tuned model`        |          **29.19**         |
 # Ethical Considerations and Limitations
+CodeLlama-7B-KStack-full and its variants are a new technology that carries risks with use. The testing conducted to date could not cover all scenarios. For these reasons, as with all LLMs, CodeLlama-7B-KStack-full's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. The model was fine-tuned on a specific data format (Kotlin tasks), and deviation from this format can also lead to inaccurate or undesirable responses to user queries. Therefore, before deploying any applications of CodeLlama-7B-KStack-full, developers should perform safety testing and tuning tailored to their specific applications of the model.