Instructions to use Transform72/PandasSolver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Transform72/PandasSolver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Transform72/PandasSolver")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Transform72/PandasSolver") model = AutoModelForCausalLM.from_pretrained("Transform72/PandasSolver") - Inference
- Local Apps Settings
- vLLM
How to use Transform72/PandasSolver with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Transform72/PandasSolver" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Transform72/PandasSolver", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Transform72/PandasSolver
- SGLang
How to use Transform72/PandasSolver 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 "Transform72/PandasSolver" \ --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": "Transform72/PandasSolver", "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 "Transform72/PandasSolver" \ --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": "Transform72/PandasSolver", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Transform72/PandasSolver with Docker Model Runner:
docker model run hf.co/Transform72/PandasSolver
How to improve it a bit more?
Hello, I am very happy with this LLM, i have use it a little bit and its way better than PandasAI, which was a disappointment.
I notice that this was a retrained model to specifics and i was surprise how good it was with matplotlib, but the score was pretty low so it will probably fail on more complex attempts.
I wonder, how much computing power do you need to push more training and make it better in Matplotlib? i want to see if i can help a bit to improve this one it seems you have a good thing here.
@masterchop Thank you for your inquiry. We are thinking of upgrading this model to boost its scores on other tasks like matplotlib as well. Please keep an eye on our published models!