Instructions to use llmixer/BigWeave-v16-103b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmixer/BigWeave-v16-103b with Transformers:

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

pipe = pipeline("text-generation", model="llmixer/BigWeave-v16-103b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmixer/BigWeave-v16-103b")
model = AutoModelForCausalLM.from_pretrained("llmixer/BigWeave-v16-103b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llmixer/BigWeave-v16-103b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmixer/BigWeave-v16-103b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmixer/BigWeave-v16-103b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llmixer/BigWeave-v16-103b

SGLang

How to use llmixer/BigWeave-v16-103b 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-v16-103b" \
    --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": "llmixer/BigWeave-v16-103b",
		"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 "llmixer/BigWeave-v16-103b" \
        --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": "llmixer/BigWeave-v16-103b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llmixer/BigWeave-v16-103b with Docker Model Runner:
```
docker model run hf.co/llmixer/BigWeave-v16-103b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BigWeave v16 103b

The BigWeave models aim to experimentally identify merge settings for increasing model performance. The version number merely tracks various attempts and is not a quality indicator. Only results demonstrating good performance are retained and shared.

Prompting Format

Mistral, Vicuna and Alpaca.

Merge process

This is a self-merge of 152334H/miqu-1-70b-sf. By conducting exl2 measurements, we identify the most relevant layers. The layers are duplicated such that each group consists of consecutive layers with a two-layer overlap (i.e. larger groups than in v15).

Merge configuration:

slices:
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [0,11]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [9,13]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [11,15]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [13,17]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [15,23]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [21,25]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [23,49]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [47,51]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [49,53]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [51,55]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [53,57]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [55,59]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [57,61]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [59,63]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [61,65]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [63,67]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [65,69]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [67,71]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [69,73]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [71,75]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [73,80]
merge_method: passthrough
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	72.02
AI2 Reasoning Challenge (25-Shot)	65.87
HellaSwag (10-Shot)	87.61
MMLU (5-Shot)	73.22
TruthfulQA (0-shot)	63.81
Winogrande (5-shot)	80.43
GSM8k (5-shot)	61.18