Instructions to use llmixer/BigWeave-v8-90b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmixer/BigWeave-v8-90b with Transformers:

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

pipe = pipeline("text-generation", model="llmixer/BigWeave-v8-90b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmixer/BigWeave-v8-90b")
model = AutoModelForCausalLM.from_pretrained("llmixer/BigWeave-v8-90b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llmixer/BigWeave-v8-90b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmixer/BigWeave-v8-90b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmixer/BigWeave-v8-90b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmixer/BigWeave-v8-90b

SGLang

How to use llmixer/BigWeave-v8-90b 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 "llmixer/BigWeave-v8-90b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmixer/BigWeave-v8-90b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "llmixer/BigWeave-v8-90b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmixer/BigWeave-v8-90b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use llmixer/BigWeave-v8-90b with Docker Model Runner:
```
docker model run hf.co/llmixer/BigWeave-v8-90b
```

YAML Metadata Warning:The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

BigWeave v8 90B

The BigWeave models aim to identify merge settings equaling or surpassing the performance of Goliath-120b. The version number merely tracks various attempts and is not a quality indicator. Only results demonstrating good performance are retained and shared.

This version is a passthrough merge of Platypus2-70b-instruct + WinterGoddess-1.4x-70b.

The 90b size allows for 4bit quants to fit into 48GB of VRAM.

Prompting Format

Vicuna and Alpaca.

Merge process

The models used in the merge are Platypus2-70b-instruct and WinterGoddess-1.4x-70b.

Acknowledgements

@garage-bAInd For creating Platypus2

@Sao10K For creating WinterGoddess

@alpindale For creating the original Goliath

@chargoddard For developing mergekit.

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Safetensors

Model size

88B params

Tensor type

F16