Instructions to use wellecks/llmstep-mathlib4-pythia2.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wellecks/llmstep-mathlib4-pythia2.8b with Transformers:

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

pipe = pipeline("text-generation", model="wellecks/llmstep-mathlib4-pythia2.8b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("wellecks/llmstep-mathlib4-pythia2.8b")
model = AutoModelForMultimodalLM.from_pretrained("wellecks/llmstep-mathlib4-pythia2.8b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wellecks/llmstep-mathlib4-pythia2.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wellecks/llmstep-mathlib4-pythia2.8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wellecks/llmstep-mathlib4-pythia2.8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wellecks/llmstep-mathlib4-pythia2.8b

SGLang

How to use wellecks/llmstep-mathlib4-pythia2.8b 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 "wellecks/llmstep-mathlib4-pythia2.8b" \
    --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": "wellecks/llmstep-mathlib4-pythia2.8b",
		"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 "wellecks/llmstep-mathlib4-pythia2.8b" \
        --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": "wellecks/llmstep-mathlib4-pythia2.8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wellecks/llmstep-mathlib4-pythia2.8b with Docker Model Runner:
```
docker model run hf.co/wellecks/llmstep-mathlib4-pythia2.8b
```

llmstep: [L]LM proofstep suggestions in Lean

https://github.com/wellecks/llmstep

This model is a Pythia-2.8b-deduped language model fine-tuned on LeanDojo Benchmark 4.

The model is fine-tuned on sequences of the form:

[GOAL]tactic-state[PROOFSTEP]next-tactic<|endoftext|>

This format corresponds to the proofstep objective from Han et al ICLR 2022.
The python/train directory in the repository shows how the model was fine-tuned.

Please see the repository for more details.

@misc{llmstep,
  author = {Sean Welleck},
  title = {llmstep: LLM proofstep suggestions in Lean},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/wellecks/llmstep}},
}

Downloads last month: 378

Paper for wellecks/llmstep-mathlib4-pythia2.8b

Proof Artifact Co-training for Theorem Proving with Language Models

Paper • 2102.06203 • Published Feb 11, 2021