---
license: apache-2.0
tags:
- moe
train: false
inference: false
pipeline_tag: text-generation
---
## Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-metaoffload-HQQ
This is a version of the Mixtral-8x7B-Instruct-v0.1 model (https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) quantized with a mix of 4-bit and 2-bit via Half-Quadratic Quantization (HQQ).

More specifically, the attention layers are quantized to 4-bit and the experts are quantized to 2-bit. 

The difference between this model and https://huggingface.co/mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-HQQ is that this one offloads the metadata to the CPU and you only need 13GB Vram to run it instead of 20GB!


### Basic Usage
To run the model, install the HQQ library from https://github.com/mobiusml/hqq and use it as follows:
``` Python
model_id = 'mobiuslabsgmbh/Mixtral-8x7B-Instruct-v0.1-hf-attn-4bit-moe-2bit-metaoffload-HQQ'
#Load the model
from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = HQQModelForCausalLM.from_quantized(model_id)
#Optional
from hqq.core.quantize import *
HQQLinear.set_backend(HQQBackend.PYTORCH_COMPILE) 
#Text Generation
prompt = "<s> [INST] How do I build a car? [/INST] "
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = model.generate(**(inputs.to('cuda')), max_new_tokens=1000)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

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</p>

### Quantization
You can reproduce the model using the following quant configs:

``` Python
from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
model_id  = "mistralai/Mixtral-8x7B-Instruct-v0.1"
model     = HQQModelForCausalLM.from_pretrained(model_id, use_auth_token=hf_auth, cache_dir=cache_path)

#Quantize params
from hqq.core.quantize import *
attn_prams     = BaseQuantizeConfig(nbits=4, group_size=64, offload_meta=True) 
experts_params = BaseQuantizeConfig(nbits=2, group_size=16, offload_meta=True) 
attn_prams['scale_quant_params']['group_size'] = 256
attn_prams['zero_quant_params']['group_size']  = 256

quant_config = {}
#Attention
quant_config['self_attn.q_proj'] = attn_prams
quant_config['self_attn.k_proj'] = attn_prams
quant_config['self_attn.v_proj'] = attn_prams
quant_config['self_attn.o_proj'] = attn_prams
#Experts
quant_config['block_sparse_moe.experts.w1'] = experts_params
quant_config['block_sparse_moe.experts.w2'] = experts_params
quant_config['block_sparse_moe.experts.w3'] = experts_params

#Quantize
model.quantize_model(quant_config=quant_config, compute_dtype=torch.float16);
model.eval();
```