Instructions to use sequelbox/Llama2-70B-StellarBright with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sequelbox/Llama2-70B-StellarBright with Transformers:

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

pipe = pipeline("text-generation", model="sequelbox/Llama2-70B-StellarBright")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sequelbox/Llama2-70B-StellarBright")
model = AutoModelForCausalLM.from_pretrained("sequelbox/Llama2-70B-StellarBright")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sequelbox/Llama2-70B-StellarBright with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sequelbox/Llama2-70B-StellarBright"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sequelbox/Llama2-70B-StellarBright",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sequelbox/Llama2-70B-StellarBright

SGLang

How to use sequelbox/Llama2-70B-StellarBright 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 "sequelbox/Llama2-70B-StellarBright" \
    --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": "sequelbox/Llama2-70B-StellarBright",
		"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 "sequelbox/Llama2-70B-StellarBright" \
        --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": "sequelbox/Llama2-70B-StellarBright",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sequelbox/Llama2-70B-StellarBright with Docker Model Runner:
```
docker model run hf.co/sequelbox/Llama2-70B-StellarBright
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Stellar Bright is a general capability upgrade to Llama 2, using open source data to improve overall knowledge, extended communication, and technical skill.

This model is primarily recommended as a superior-to-Llama-2 baseline for additional finetuning, not for direct deployment to production as a chat model. The user accepts full responsibility for all outputs.

This is a 'legacy model' offered primarily for reference purposes. I recommend Llama 3 over this model for general use.

Evaluation

Model	Avg	ARC	HS	MMLU	TQA
Stellar Bright	74.10	72.95	87.82	71.17	64.46
Llama 2	67.35	67.32	87.33	69.83	44.92
Llama 2 Chat	66.80	64.59	85.88	63.91	52.80

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Safetensors

Model size

69B params

Tensor type

F32

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Quantizations

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Llama2-70B-StellarBright

Evaluation

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