Instructions to use llmixer/BigWeave-v9-90b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmixer/BigWeave-v9-90b with Transformers:

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

pipe = pipeline("text-generation", model="llmixer/BigWeave-v9-90b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmixer/BigWeave-v9-90b")
model = AutoModelForCausalLM.from_pretrained("llmixer/BigWeave-v9-90b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llmixer/BigWeave-v9-90b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmixer/BigWeave-v9-90b"
# 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-v9-90b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmixer/BigWeave-v9-90b

SGLang

How to use llmixer/BigWeave-v9-90b 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-v9-90b" \
    --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-v9-90b",
		"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-v9-90b" \
        --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-v9-90b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use llmixer/BigWeave-v9-90b with Docker Model Runner:
```
docker model run hf.co/llmixer/BigWeave-v9-90b
```

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 v9 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 DARE-TIES merge of two passthrough merges: Xwin-LM-70b-v0.1 + Euryale-1.3-70b (BigWeave v6) and Platypus2-70b-instruct + WinterGoddess-1.4x-70b (BigWeave v8). Both models individually show strong performance, and the merged model achieves even lower perplexity than each model separately.

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 Xwin-LM-70b-v0.1, Euryale-1.3-70b, Platypus2-70b-instruct and WinterGoddess-1.4x-70b.

Merge configuration: ``` slices: - sources: - model: Xwin-LM/Xwin-LM-70B-V0.1 layer_range: [0,12] - sources: - model: Sao10K/Euryale-1.3-L2-70B layer_range: [9,14] - sources: - model: Xwin-LM/Xwin-LM-70B-V0.1 layer_range: [12,62] - sources: - model: Sao10K/Euryale-1.3-L2-70B layer_range: [54,71] - sources: - model: Xwin-LM/Xwin-LM-70B-V0.1 layer_range: [62,80] merge_method: passthrough dtype: float16

slices: - sources: - model: garage-bAInd/Platypus2-70B-instruct layer_range: [0,12] - sources: - model: Sao10K/WinterGoddess-1.4x-70B-L2 layer_range: [9,14] - sources: - model: garage-bAInd/Platypus2-70B-instruct layer_range: [12,62] - sources: - model: Sao10/WinterGoddess-1.4x-70B-L2 layer_range: [54,71] - sources: - model: garage-bAInd/Platypus2-70B-instruct layer_range: [62,80] merge_method: passthrough dtype: float16

models: - model: llmixer/BigWeave-v8-90b parameters: weight: 0.5 density: 0.5 merge_method: dare_ties base_model: llmixer/BigWeave-v6-90b dtype: float16


# Acknowledgements
[@Xwin-LM](https://huggingface.co/Xwin-LM) For creating Xwin

[@Sao10K](https://huggingface.co/Sao10K) For creating Euryale and WinterGoddess

[@garage-bAInd](https://huggingface.co/garage-bAInd) For creating Platypus2

[@alpindale](https://huggingface.co/alpindale) For creating the original Goliath

[@chargoddard](https://huggingface.co/chargoddard) For developing [mergekit](https://github.com/cg123/mergekit).

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Safetensors

Model size

88B params

Tensor type

F16

Collection including llmixer/BigWeave-v9-90b

BigWeave Viable

Collection

All viable models from the BigWeave series, excluding quantized versions • 7 items • Updated Feb 15, 2024