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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

llmixer commited on Jan 23, 2024

Commit

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+---
+license: llama2
+language:
+- en
+pipeline_tag: conversational
+tags:
+- Platypus2
+- WinterGoddess
+- frankenmerge
+- dare
+- ties
+- 90b
+---
+# BigWeave v8 90B
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65a6db055c58475cf9e6def1/4CbbAN-X7ZWj702JrcCGH.png" width=600>
+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](https://huggingface.co/garage-bAInd/Platypus2-70B-instruct) and [WinterGoddess-1.4x-70b](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2).
+# Acknowledgements
+[@garage-bAInd](https://huggingface.co/garage-bAInd) For creating Platypus2
+[@Sao10K](https://huggingface.co/Sao10K) For creating WinterGoddess
+[@alpindale](https://huggingface.co/alpindale) For creating the original Goliath
+[@chargoddard](https://huggingface.co/chargoddard) For developing [mergekit](https://github.com/cg123/mergekit).