Instructions to use HaimingW/Leandojo-CodeLLama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HaimingW/Leandojo-CodeLLama-7b with Transformers:

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

pipe = pipeline("text-generation", model="HaimingW/Leandojo-CodeLLama-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HaimingW/Leandojo-CodeLLama-7b")
model = AutoModelForCausalLM.from_pretrained("HaimingW/Leandojo-CodeLLama-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HaimingW/Leandojo-CodeLLama-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HaimingW/Leandojo-CodeLLama-7b

SGLang

How to use HaimingW/Leandojo-CodeLLama-7b 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 "HaimingW/Leandojo-CodeLLama-7b" \
    --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": "HaimingW/Leandojo-CodeLLama-7b",
		"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 "HaimingW/Leandojo-CodeLLama-7b" \
        --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": "HaimingW/Leandojo-CodeLLama-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HaimingW/Leandojo-CodeLLama-7b with Docker Model Runner:
```
docker model run hf.co/HaimingW/Leandojo-CodeLLama-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Check out the documentation for more information.

This is a CodeLlama-7b model fine-tuned on LeanDojo Benchmark

The performance is comparable with the paper Temperature-scaled large language models for Lean proofstep prediction with around 57.7 pass@1 accuracy on LeanDojo "random" benchmark.

The training lines are formatted as follow:

GOAL $(tactic state) PROOFSTEP $(tactic)

For inference, use the following line:

GOAL $(tactic state) PROOFSTEP

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for HaimingW/Leandojo-CodeLLama-7b

Quantizations

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