Instructions to use GOAT-AI/GOAT-7B-Community with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GOAT-AI/GOAT-7B-Community with Transformers:

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

pipe = pipeline("text-generation", model="GOAT-AI/GOAT-7B-Community")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GOAT-AI/GOAT-7B-Community")
model = AutoModelForCausalLM.from_pretrained("GOAT-AI/GOAT-7B-Community")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GOAT-AI/GOAT-7B-Community with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/GOAT-AI/GOAT-7B-Community

SGLang

How to use GOAT-AI/GOAT-7B-Community 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 "GOAT-AI/GOAT-7B-Community" \
    --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": "GOAT-AI/GOAT-7B-Community",
		"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 "GOAT-AI/GOAT-7B-Community" \
        --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": "GOAT-AI/GOAT-7B-Community",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GOAT-AI/GOAT-7B-Community with Docker Model Runner:
```
docker model run hf.co/GOAT-AI/GOAT-7B-Community
```

goatai

leaderboard-pr-bot commited on Nov 19, 2023

Commit

c952236

1 Parent(s): 8a5bda1

Adding Evaluation Results (#3)

Browse files

- Adding Evaluation Results (fa3d45ba2653420f5e471678d6cca6727ef5cdd8)

Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -68,4 +68,17 @@ GOAT-7B-Community model weights are available under LLAMA-2 license. Note that t
 ### Risks and Biases
-GOAT-7B-Community model can produce factually incorrect output and should not be relied on to deliver factually accurate information. The model was trained on various private and public datasets. Therefore, the GOAT-7B-Community model could possibly generate wrong, biased, or otherwise offensive outputs.

 ### Risks and Biases
+GOAT-7B-Community model can produce factually incorrect output and should not be relied on to deliver factually accurate information. The model was trained on various private and public datasets. Therefore, the GOAT-7B-Community model could possibly generate wrong, biased, or otherwise offensive outputs.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_GOAT-AI__GOAT-7B-Community)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 42.74   |
+| ARC (25-shot)         | 48.81          |
+| HellaSwag (10-shot)   | 74.63    |
+| MMLU (5-shot)         | 49.58         |
+| TruthfulQA (0-shot)   | 42.48   |
+| Winogrande (5-shot)   | 72.3   |
+| GSM8K (5-shot)        | 4.47        |
+| DROP (3-shot)         | 6.91         |