Instructions to use R136a1/BeyondInfinity-4x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use R136a1/BeyondInfinity-4x7B with Transformers:

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

pipe = pipeline("text-generation", model="R136a1/BeyondInfinity-4x7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("R136a1/BeyondInfinity-4x7B")
model = AutoModelForCausalLM.from_pretrained("R136a1/BeyondInfinity-4x7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use R136a1/BeyondInfinity-4x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "R136a1/BeyondInfinity-4x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "R136a1/BeyondInfinity-4x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/R136a1/BeyondInfinity-4x7B

SGLang

How to use R136a1/BeyondInfinity-4x7B 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 "R136a1/BeyondInfinity-4x7B" \
    --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": "R136a1/BeyondInfinity-4x7B",
		"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 "R136a1/BeyondInfinity-4x7B" \
        --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": "R136a1/BeyondInfinity-4x7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use R136a1/BeyondInfinity-4x7B with Docker Model Runner:
```
docker model run hf.co/R136a1/BeyondInfinity-4x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Testing done.

It performs really well in complex scenario and follows the character card quite well. The char card and previous message can affect a lot to the next reply style.

The main idea is instead of merging models to create new model, I try to put these best model into mixtral so it can work together. And the result is good, every model has its uniqueness and strength.

Downside? it only support 8k (8192) context length...

Alpaca prompting format.

Downloads last month: 21

Safetensors

Model size

24B params

Tensor type

BF16

Model tree for R136a1/BeyondInfinity-4x7B

Quantizations

4 models