Instructions to use DiscoResearch/mixtral-7b-8expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DiscoResearch/mixtral-7b-8expert with Transformers:

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

pipe = pipeline("text-generation", model="DiscoResearch/mixtral-7b-8expert", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DiscoResearch/mixtral-7b-8expert", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DiscoResearch/mixtral-7b-8expert", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DiscoResearch/mixtral-7b-8expert with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DiscoResearch/mixtral-7b-8expert"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DiscoResearch/mixtral-7b-8expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DiscoResearch/mixtral-7b-8expert

SGLang

How to use DiscoResearch/mixtral-7b-8expert 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 "DiscoResearch/mixtral-7b-8expert" \
    --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": "DiscoResearch/mixtral-7b-8expert",
		"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 "DiscoResearch/mixtral-7b-8expert" \
        --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": "DiscoResearch/mixtral-7b-8expert",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DiscoResearch/mixtral-7b-8expert with Docker Model Runner:
```
docker model run hf.co/DiscoResearch/mixtral-7b-8expert
```

Flash dependency (locks out non-NVIDIA GPUs)

by Thalesian - opened Dec 10, 2023

Discussion

Thalesian

Dec 10, 2023

Title says it all. This should run on a Mac M1 architecture (some have VRAM > 98, so can run this). However flash_attn is called on repeatedly, and hard to re-code without it.

bjoernp

Disco Research org Dec 10, 2023

The code is equivalent to the standard mistral 7b code other than the MoE integration which does not use attention. Flash attention should only be used when loading the model with use_flash_attn_2=True, otherwise it should be good. Have you tried it?

Thalesian

Dec 10, 2023

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("~/LLM/mixtral-8x7b-32kseqlen", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True)

Traceback (most recent call last):
File "", line 1, in
File "/miniconda3/envs/textgen/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 553, in from_pretrained
model_class = get_class_from_dynamic_module(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/textgen/lib/python3.11/site-packages/transformers/dynamic_module_utils.py", line 487, in get_class_from_dynamic_module
final_module = get_cached_module_file(
^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/textgen/lib/python3.11/site-packages/transformers/dynamic_module_utils.py", line 314, in get_cached_module_file
modules_needed = check_imports(resolved_module_file)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/miniconda3/envs/textgen/lib/python3.11/site-packages/transformers/dynamic_module_utils.py", line 179, in check_imports
raise ImportError(
ImportError: This modeling file requires the following packages that were not found in your environment: flash_attn. Run pip install flash_attn

Not sure why it keeps trying to get flash attention - this hasn't been a problem with other models.

mrfakename

Dec 10, 2023

Title says it all. This should run on a Mac M1 architecture (some have VRAM > 98, so can run this). However flash_attn is called on repeatedly, and hard to re-code without it.

Some have VRAM > 98 GB for CPU or GPU?

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