Instructions to use llmixer/BigWeave-v15-103b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmixer/BigWeave-v15-103b with Transformers:

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

pipe = pipeline("text-generation", model="llmixer/BigWeave-v15-103b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmixer/BigWeave-v15-103b")
model = AutoModelForCausalLM.from_pretrained("llmixer/BigWeave-v15-103b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llmixer/BigWeave-v15-103b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmixer/BigWeave-v15-103b"
# 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-v15-103b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmixer/BigWeave-v15-103b

SGLang

How to use llmixer/BigWeave-v15-103b 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-v15-103b" \
    --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-v15-103b",
		"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-v15-103b" \
        --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-v15-103b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use llmixer/BigWeave-v15-103b with Docker Model Runner:
```
docker model run hf.co/llmixer/BigWeave-v15-103b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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 v15 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. These layers are then duplicated in pairs to ensure overlaps.

Merge configuration:

slices:
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [0,3]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [1,5]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [3,7]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [5,9]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [7,18]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [16,21]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [19,27]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [25,30]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [28,32]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [30,34]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [32,36]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [34,38]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [36,40]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [38,42]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [40,44]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [42,46]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [44,48]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [46,51]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [49,77]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [75,79]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [77,80]
merge_method: passthrough
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.67
AI2 Reasoning Challenge (25-Shot)	69.71
HellaSwag (10-Shot)	86.41
MMLU (5-Shot)	71.25
TruthfulQA (0-shot)	66.10
Winogrande (5-shot)	80.35
GSM8k (5-shot)	56.18

Downloads last month: 66

Safetensors

Model size

103B params

Tensor type

F16

Model tree for llmixer/BigWeave-v15-103b

Quantizations

2 models

Collection including llmixer/BigWeave-v15-103b

BigWeave Viable

Collection

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

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

69.710
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.410
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

71.250
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

66.100
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

80.350
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

56.180