Instructions to use Weyaxi/CollectiveCognition-v1.1-Nebula-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Weyaxi/CollectiveCognition-v1.1-Nebula-7B with Transformers:

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

pipe = pipeline("text-generation", model="Weyaxi/CollectiveCognition-v1.1-Nebula-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Weyaxi/CollectiveCognition-v1.1-Nebula-7B")
model = AutoModelForCausalLM.from_pretrained("Weyaxi/CollectiveCognition-v1.1-Nebula-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Weyaxi/CollectiveCognition-v1.1-Nebula-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Weyaxi/CollectiveCognition-v1.1-Nebula-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Weyaxi/CollectiveCognition-v1.1-Nebula-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Weyaxi/CollectiveCognition-v1.1-Nebula-7B

SGLang

How to use Weyaxi/CollectiveCognition-v1.1-Nebula-7B 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 "Weyaxi/CollectiveCognition-v1.1-Nebula-7B" \
    --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": "Weyaxi/CollectiveCognition-v1.1-Nebula-7B",
		"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 "Weyaxi/CollectiveCognition-v1.1-Nebula-7B" \
        --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": "Weyaxi/CollectiveCognition-v1.1-Nebula-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Weyaxi/CollectiveCognition-v1.1-Nebula-7B with Docker Model Runner:
```
docker model run hf.co/Weyaxi/CollectiveCognition-v1.1-Nebula-7B
```

CollectiveCognition-v1.1-Nebula-7B

File size: 1,224 Bytes

346b447
a47d8b2
6ed3bcb
 
 
 
346b447
6ed3bcb
346b447
6ed3bcb
346b447
6ed3bcb
346b447
e08f943

---
license: cc-by-nc-4.0
datasets:
- garage-bAInd/Open-Platypus
language:
- en
---
<a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>

# OpenOrca-Nebula-7B

OpenOrca-Nebula-7B is a merge of [teknium/CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B) and [PulsarAI/Nebula-7B](https://huggingface.co/Weyaxi/PulsarAI/Nebula-7B)

# [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_PulsarAI__CollectiveCognition-v1.1-Nebula-7B)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 53.79   |
| ARC (25-shot)         | 58.11          |
| HellaSwag (10-shot)   | 82.39    |
| MMLU (5-shot)         | 57.03         |
| TruthfulQA (0-shot)   | 53.53   |
| Winogrande (5-shot)   | 73.72   |
| GSM8K (5-shot)        | 9.55        |
| DROP (3-shot)         | 42.17         |