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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

BigWeave-v15-103b / README.md

llmixer

Adding Evaluation Results (#2)

fee2633 verified over 2 years ago

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metadata

language:
  - en
license: unknown
tags:
  - frankenmerge
  - 103b
pipeline_tag: conversational
model-index:
  - name: BigWeave-v15-103b
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 69.71
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=llmixer/BigWeave-v15-103b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 86.41
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=llmixer/BigWeave-v15-103b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 71.25
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=llmixer/BigWeave-v15-103b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 66.1
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=llmixer/BigWeave-v15-103b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 80.35
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=llmixer/BigWeave-v15-103b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 56.18
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=llmixer/BigWeave-v15-103b
          name: Open LLM Leaderboard

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