Instructions to use llm-agents/tora-code-7b-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llm-agents/tora-code-7b-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="llm-agents/tora-code-7b-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llm-agents/tora-code-7b-v1.0")
model = AutoModelForCausalLM.from_pretrained("llm-agents/tora-code-7b-v1.0")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llm-agents/tora-code-7b-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llm-agents/tora-code-7b-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-agents/tora-code-7b-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llm-agents/tora-code-7b-v1.0

SGLang

How to use llm-agents/tora-code-7b-v1.0 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 "llm-agents/tora-code-7b-v1.0" \
    --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": "llm-agents/tora-code-7b-v1.0",
		"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 "llm-agents/tora-code-7b-v1.0" \
        --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": "llm-agents/tora-code-7b-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llm-agents/tora-code-7b-v1.0 with Docker Model Runner:
```
docker model run hf.co/llm-agents/tora-code-7b-v1.0
```

Librarian Bot: Update Hugging Face dataset ID

by librarian-bot - opened Apr 11, 2024

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-7

librarian-bot

Apr 11, 2024

This pull request updates the ID of the dataset used to train the model to the new Hub identifier hendrycks/competition_math (which has been migrated moved from competition_math). We have been working to migrate datasets to their own repositories on the Hub, and this is part of that effort.

Updating the dataset ID in the model card will ensure that the model card is correctly linked to the dataset repository on the Hub. This will also make it easier for people to find your model via the training data used to create it.

This PR comes courtesy of Librarian Bot. If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien.

Librarian Bot: Update Hugging Face dataset ID29a0e004

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