| language: | |
| - fr | |
| - it | |
| - de | |
| - es | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - moe | |
| inference: false | |
| # Model Card for Mixtral-Extraction-4x7B-Instruct-v0.1 | |
| This model is an experimental model created by merging [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co./mistralai/Mixtral-8x7B-Instruct-v0.1) experts. | |
| # How we extracted experts | |
| Experts are selected and extracted. | |
| This model specifies 4 experts. | |
| # How To Convert | |
| use colab cpu-high-memory. | |
| You can extract experts 1-7 by selecting experts as bit string. | |
| ~~~python | |
| experts_extract_bit = "11110000" | |
| ~~~ | |
| [convert_mixtral_8x7b_to_4x7b_extract.ipynb](https://huggingface.co./mmnga/Mixtral-Extraction-4x7B-Instruct-v0.1/blob/main/notebook/convert_mixtral_8x7b_to_4x7b_extract.ipynb) | |
| # Usage | |
| ~~~python | |
| pip install git+https://github.com/huggingface/transformers --upgrade | |
| pip install torch accelerate bitsandbytes flash_attn | |
| ~~~ | |
| ~~~python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, MixtralForCausalLM | |
| import torch | |
| model_name_or_path = "mmnga/Mixtral-Extraction-4x7B-Instruct-v0.1" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) | |
| model = MixtralForCausalLM.from_pretrained(model_name_or_path, load_in_8bit=True) | |
| text = "[INST] What was John Holt's vision on education? [/INST] " | |
| inputs = tokenizer("<s> " + text, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=128) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| ~~~ |