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

jdev8 commited on May 15, 2024

Commit

a6f080d

verified ·

1 Parent(s): b703bd3

Update README.md (#1)

Browse files

- Update README.md (704093fb3d36ad12d970386311c3473ba65d0ca5)

Co-authored-by: Anton Shapkin <jdev8@users.noreply.huggingface.co>

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README.md +8 -0

README.md CHANGED Viewed

@@ -10,6 +10,14 @@ This is CodeLlama model fine-tuned on Kotlin Exercices dataset.
 The model was trained on one A100 GPU with following hyperparameters:
 # Fine-tuning data
 For this model we used 15K exmaples of Kotlin Exercices dataset. For more information about the dataset follow th link.

 The model was trained on one A100 GPU with following hyperparameters:
+|         **Hyperparameter**           |             **Value**              |
+|:---------------------------:|:----------------------------------------:|
+|           `warmup`            |           10%            |
+|        `max_lr`        |          1e-4          |
+|        `scheduler`        |          linear          |
+|        `total_batch_size`        |          256 (~130K tokens per step)          |
 # Fine-tuning data
 For this model we used 15K exmaples of Kotlin Exercices dataset. For more information about the dataset follow th link.