Instructions to use jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2")
model = AutoModelForMultimodalLM.from_pretrained("jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2

SGLang

How to use jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2 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 "jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2" \
    --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": "jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2",
		"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 "jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2" \
        --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": "jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2 with Docker Model Runner:
```
docker model run hf.co/jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2
```

Should be working.

Test merge of two extremely large MoE models using SLERP. Don't know if it's working correctly yet, haven't had the time or hardware to test.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


slices:
  - sources:
      - model: mistralai/Mixtral-8x22B-Instruct-v0.1
        layer_range: [0, 56]
      - model: alpindale/WizardLM-2-8x22B
        layer_range: [0, 56]
merge_method: slerp
base_model: mistralai/Mixtral-8x22B-Instruct-v0.1
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Downloads last month: 3

Safetensors

Model size

141B params

Tensor type

BF16

Model tree for jsfs11/MixtralxWizardLM2-8x22B-SLERP-v0.2

alpindale/WizardLM-2-8x22B

mistralai/Mixtral-8x22B-Instruct-v0.1

Merge model

this model