Text Generation
Transformers
PyTorch
English
llama
code
Eval Results (legacy)
text-generation-inference
Instructions to use pipizhao/Pandalyst-7B-V1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pipizhao/Pandalyst-7B-V1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pipizhao/Pandalyst-7B-V1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pipizhao/Pandalyst-7B-V1.1") model = AutoModelForCausalLM.from_pretrained("pipizhao/Pandalyst-7B-V1.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use pipizhao/Pandalyst-7B-V1.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pipizhao/Pandalyst-7B-V1.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pipizhao/Pandalyst-7B-V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pipizhao/Pandalyst-7B-V1.1
- SGLang
How to use pipizhao/Pandalyst-7B-V1.1 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 "pipizhao/Pandalyst-7B-V1.1" \ --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": "pipizhao/Pandalyst-7B-V1.1", "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 "pipizhao/Pandalyst-7B-V1.1" \ --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": "pipizhao/Pandalyst-7B-V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pipizhao/Pandalyst-7B-V1.1 with Docker Model Runner:
docker model run hf.co/pipizhao/Pandalyst-7B-V1.1
Create README.md
Browse files
README.md
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---
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license: llama2
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library_name: transformers
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tags:
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- code
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model-index:
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- name: Pandalyst_7B_v1.1
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results:
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- task:
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type: text-generation
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metrics:
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- name: exec@1
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type: exec@1
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value: 0.76
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verified: false
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language:
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- en
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---
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## Pandalyst: A large language model for mastering data analysis using pandas
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<p align="center">
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<img src="https://raw.githubusercontent.com/zhengyanzhao1997/Pandalyst/master/imgs/pandalyst.png" width="300"/>
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</p>
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<p align="center">
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🐱 <a href="https://github.com/zhengyanzhao1997/Pandalyst" target="_blank">Github Repo</a> <br>
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</p>
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**What is Pandalyst**
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- Pandalyst is a general large language model specifically trained to process and analyze data using the pandas library.
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**How is Pandalyst**
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- Pandalyst has strong generalization capabilities for data tables in different fields and different data analysis needs.
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**Why is Pandalyst**
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- Pandalyst is open source and free to use, and its small parameter size (7B/13B) allows us to easily deploy it on local PC.
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- Pandalyst can handle complex data tables (multiple columns and multiple rows), allowing us to enter enough context to describe our table in detail.
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- Pandalyst has very competitive performance, significantly outperforming models of the same size and even outperforming some of the strongest closed-source models.
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## News
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- 🔥[2023/09/30] We released **Pandalyst-7B-V1.1** , which was trained on **CodeLlama-7b-Python** and achieves the **76.1 exec@1** in our **PandaTest_V1.0** and surpasses **Pandalyst-13B-V1.0**, **WizardCoder-Python-13B-V1.0** and **ChatGPT-3.5 (2023/06/13)**.
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- 🔥[2023/09/28] We released **Pandalyst-13B-V1.0** , which was trained on **WizardCoder-Python-13B-V1.0** and achieves the **70.7 exec@1** in our **PandaTest_V1.0** and surpasses **WizardCoder-Python-13B-V1.0** and **ChatGPT-3.5 (2023/06/13)**.
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| Model | Checkpoint | Base Model | PandaTest_V1.0 | EASY | HARD | License |
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|--------------------|---------------------------------------------------------------------------------------------|------------|----------------|---------------------|---------------------| ----- |
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| Pandalyst-13B-V1.0 | 🤗 <a href="https://huggingface.co/pipizhao/Pandalyst_13B_V1.0" target="_blank">HF Link</a> | WizardCoder-Python-13B-V1.0 | 70.7 | 75.6 | 65.9 | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
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| Pandalyst-7B-V1.1 | 🤗 <a href="https://huggingface.co/pipizhao/Pandalyst-7B-V1.1" target="_blank">HF Link</a> | CodeLlama-7b-Python | 76.1 | 85.2 | 67.0 | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
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## Usage
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Please refer to <a href="https://github.com/zhengyanzhao1997/Pandalyst" target="_blank">Github</a>.
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