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
```

jdev8 commited on May 16, 2024

Commit

cda30cc

verified ·

1 Parent(s): 8edc10e

Update README.md

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README.md +12 -11

README.md CHANGED Viewed

@@ -2,16 +2,17 @@
 license: apache-2.0
 datasets:
 - JetBrains/KExercises
 results:
-  - task:
-      type: text-generation
-    dataset:
-      name: MultiPL-HumanEval (Kotlin)
-      type: openai_humaneval
-    metrics:
-      - name: pass@1
-        type: pass@1
-        value: 42.24
 tags:
 - code
 ---
@@ -72,12 +73,12 @@ More details about finetuning can be found in the technical report
 # Fine-tuning data
-For this model we used 15K exmaples of Kotlin Exercices dataset {TODO: link!}. Every example follows HumanEval like format. In total dataset contains about 3.5M tokens.
 For more information about the dataset follow the link.
 # Evaluation
-To evaluate we used Kotlin Humaneval (more infromation here)
 Fine-tuned model:

 license: apache-2.0
 datasets:
 - JetBrains/KExercises
+base_model: meta-llama/CodeLlama-7b-hf
 results:
+- task:
+    type: text-generation
+  dataset:
+    name: MultiPL-HumanEval (Kotlin)
+    type: openai_humaneval
+  metrics:
+  - name: pass@1
+    type: pass@1
+    value: 42.24
 tags:
 - code
 ---
 # Fine-tuning data
+For this model we used 15K exmaples of [Kotlin Exercices dataset](https://huggingface.co/datasets/JetBrains/KExercises). Every example follows HumanEval like format. In total dataset contains about 3.5M tokens.
 For more information about the dataset follow the link.
 # Evaluation
+To evaluate we used Kotlin Humaneval ([more infromation here](https://huggingface.co/datasets/JetBrains/Kotlin_HumanEval))
 Fine-tuned model: