Instructions to use maicomputer/toolpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maicomputer/toolpaca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maicomputer/toolpaca")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("maicomputer/toolpaca") model = AutoModelForCausalLM.from_pretrained("maicomputer/toolpaca") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use maicomputer/toolpaca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maicomputer/toolpaca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maicomputer/toolpaca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/maicomputer/toolpaca
- SGLang
How to use maicomputer/toolpaca 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 "maicomputer/toolpaca" \ --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": "maicomputer/toolpaca", "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 "maicomputer/toolpaca" \ --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": "maicomputer/toolpaca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use maicomputer/toolpaca with Docker Model Runner:
docker model run hf.co/maicomputer/toolpaca
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
# ToolPaca
|
| 2 |
-
|
| 3 |
-
based off https://huggingface.co/chavinlo/gpt4-x-alpaca
|
| 4 |
-
|
| 5 |
-
""""""""""toolformer""""""""""
|
| 6 |
-
|
| 7 |
-
sample:
|
| 8 |
-
|
| 9 |
-
```json
|
| 10 |
-
{
|
| 11 |
-
"instruction": "toolformer: enabled\ntoolformer access: python\nA Python shell. Use this to execute python commands. Input should be a valid python command or script. If you expect output it should be printed out. Useful for all code, as well as math calculations.\npython(codetoexecute)\nFind the greatest common divisor of the given pair of integers.",
|
| 12 |
-
"input": "48, 36",
|
| 13 |
-
"response": "The greatest common divisor is python('import math; math.gcd(48, 36)')."
|
| 14 |
-
}
|
| 15 |
-
```
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
dataset: https://cdn.discordapp.com/attachments/1088641238485442661/1090460649596919878/toolformer-similarity-0.9-dataset.json
|
| 19 |
-
|
| 20 |
-
NO LORA
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|