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--- |
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language: |
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- en |
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- es |
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- de |
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- it |
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- fr |
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tags: |
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- quantized |
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- 4-bit |
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- AWQ |
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- transformers |
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- safetensors |
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- mixtral |
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- text-generation |
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- moe |
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- fr |
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- it |
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- de |
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- es |
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- en |
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- license:apache-2.0 |
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- autotrain_compatible |
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- endpoints_compatible |
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- text-generation-inference |
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- region:us |
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base_model: v2ray/Mixtral-8x22B-v0.1 |
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model_name: Mixtral-8x22B-v0.1-AWQ |
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inference: false |
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model_creator: v2ray |
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pipeline_tag: text-generation |
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quantized_by: MaziyarPanahi |
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--- |
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<img src="./mixtral-8x22b.jpeg" width="600" /> |
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# Mixtral-8x22B-v0.1-AWQ |
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On April 10th, [@MistralAI](https://huggingface.co/mistralai) released a model named "Mixtral 8x22B," an 176B MoE via magnet link (torrent): |
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- 176B MoE with ~40B active |
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- Context length of 65k tokens |
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- The base model can be fine-tuned |
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- Requires ~260GB VRAM in fp16, 73GB in int4 |
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- Licensed under Apache 2.0, according to their Discord |
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- Available on @huggingface (community) |
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- Utilizes a tokenizer similar to previous models |
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[MaziyarPanahi/Mixtral-8x22B-v0.1-AWQ](https://huggingface.co/MaziyarPanahi/Mixtral-8x22B-v0.1-AWQ) is a quantized (AWQ) version of [v2ray/Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) |
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## How to use |
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### Install the necessary packages |
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``` |
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pip install --upgrade accelerate autoawq transformers |
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``` |
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### Example Python code |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_id = "MaziyarPanahi/Mixtral-8x22B-v0.1-AWQ" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id).to(0) |
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text = "Hello can you provide me with top-3 cool places to visit in Paris?" |
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inputs = tokenizer(text, return_tensors="pt").to(0) |
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out = model.generate(**inputs, max_new_tokens=300) |
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print(tokenizer.decode(out[0], skip_special_tokens=True)) |
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``` |
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## Credit |
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- [MistralAI](https://huggingface.co/mistralai) for opening the weights |
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- [v2ray](https://huggingface.co/v2ray/) for downloading, converting, and sharing it with the community [Mixtral-8x22B-v0.1](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1) |
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- [philschmid](https://huggingface.co/philschmid) for the photo he shared on his Twitter |
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