Instructions to use llmixer/BigWeave-v26-95b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmixer/BigWeave-v26-95b with Transformers:

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

pipe = pipeline("text-generation", model="llmixer/BigWeave-v26-95b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmixer/BigWeave-v26-95b")
model = AutoModelForCausalLM.from_pretrained("llmixer/BigWeave-v26-95b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llmixer/BigWeave-v26-95b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmixer/BigWeave-v26-95b"
# 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-v26-95b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llmixer/BigWeave-v26-95b

SGLang

How to use llmixer/BigWeave-v26-95b 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-v26-95b" \
    --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-v26-95b",
		"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-v26-95b" \
        --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-v26-95b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llmixer/BigWeave-v26-95b with Docker Model Runner:
```
docker model run hf.co/llmixer/BigWeave-v26-95b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BigWeave v26 95b

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

Chatml, Mistral, Vicuna.

Merge process

This is a self-merge of 152334H/miqu-1-70b-sf. The last 30 layers are duplicated in groups of 10 layers. According to exl2 measurements, these are among the most important layers.

Merge configuration:

slices:
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [0,54]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [49,59]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [54,64]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [59,69]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [64,74]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [69,79]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [74,80]
merge_method: passthrough
dtype: float16

Downloads last month: 69

Safetensors

Model size

95B params

Tensor type

F16

Model tree for llmixer/BigWeave-v26-95b

Base model

152334H/miqu-1-70b-sf

Finetuned

(25)

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Quantizations

2 models