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---
license: apache-2.0
---
# Mixtral 7b 8 Expert
This is a preliminary HuggingFace implementation of the newly released MoE model by MistralAi. Make sure to load with `trust_remote_code=True`.
Thanks to @dzhulgakov for his early implementation (https://github.com/dzhulgakov/llama-mistral) that helped me find a working setup.
# Basic Inference setup
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("DiscoResearch/mixtral-7b-8expert", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("DiscoResearch/mixtral-7b-8expert")
x = tok.encode("The mistral wind in is a phenomenon ", return_tensors="pt").cuda()
x = model.generate(x, max_new_tokens=128).cpu()
print(tok.batch_decode(x))
```
# Conversion
Use `convert_mistral_moe_weights_to_hf.py --input_dir ./input_dir --model_size 7B --output_dir ./output` to convert the original consolidated weights to this HF setup.
Come chat about this in our [Disco(rd)](https://discord.gg/S8W8B5nz3v)! :) |