Instructions to use Lajonbot/tableBeluga-7B-instruct-pl-lora_unload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lajonbot/tableBeluga-7B-instruct-pl-lora_unload with Transformers:

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

pipe = pipeline("text-generation", model="Lajonbot/tableBeluga-7B-instruct-pl-lora_unload")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lajonbot/tableBeluga-7B-instruct-pl-lora_unload")
model = AutoModelForCausalLM.from_pretrained("Lajonbot/tableBeluga-7B-instruct-pl-lora_unload")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lajonbot/tableBeluga-7B-instruct-pl-lora_unload with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Lajonbot/tableBeluga-7B-instruct-pl-lora_unload

SGLang

How to use Lajonbot/tableBeluga-7B-instruct-pl-lora_unload 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 "Lajonbot/tableBeluga-7B-instruct-pl-lora_unload" \
    --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": "Lajonbot/tableBeluga-7B-instruct-pl-lora_unload",
		"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 "Lajonbot/tableBeluga-7B-instruct-pl-lora_unload" \
        --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": "Lajonbot/tableBeluga-7B-instruct-pl-lora_unload",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lajonbot/tableBeluga-7B-instruct-pl-lora_unload with Docker Model Runner:
```
docker model run hf.co/Lajonbot/tableBeluga-7B-instruct-pl-lora_unload
```

leaderboard-pr-bot commited on Nov 17, 2023

Commit

bd01aca

1 Parent(s): eeb22ca

Adding Evaluation Results

Browse files

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -12,4 +12,17 @@ tags:
 - pytorch
 - llama
 - llama-2
----

 - pytorch
 - llama
 - llama-2
+---
+# [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_Lajonbot__tableBeluga-7B-instruct-pl-lora_unload)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 48.02   |
+| ARC (25-shot)         | 56.23          |
+| HellaSwag (10-shot)   | 79.12    |
+| MMLU (5-shot)         | 52.7         |
+| TruthfulQA (0-shot)   | 50.19   |
+| Winogrande (5-shot)   | 75.22   |
+| GSM8K (5-shot)        | 7.81        |
+| DROP (3-shot)         | 14.86         |