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
```

Aspr commited on May 16, 2024

Commit

41cd853

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@@ -18,9 +18,20 @@ tags:
 # KStack-full models
-KStack-full models is a collection of fine-tuned open-source generative text models fine-tuned on KStack dataset.
 This is a repository for fine-tuned CodeLlama-7b model in the Hugging Face Transformers format.
 # Model use
 ```python

 # KStack-full models
+KStack-full models is a collection of fine-tuned open-source generative text models fine-tuned on KStack dataset with rule-based filtering.
 This is a repository for fine-tuned CodeLlama-7b model in the Hugging Face Transformers format.
+## Rule-based filtering
+To increase the quality of the dataset and filter out statistical outliers such as homework assignments, we filter out the dataset entries according to the following rules:
+* We filter out files which belong to the low-popular repos (the sum of stars and forks is less than 6)
+* Next, we filter out files which belong to the repos with less than 5 Kotlin files
+* Finally, we remove files which have less than 20 SLOC
+We clean the content of the remaining dataset entries according to the following rules:
+* We remove all non-ASCII entries
+* We remove all package lines such as _package kotlinx.coroutines.channels_
+* We remove half of the import lines.
 # Model use
 ```python