--- license: cc-by-sa-4.0 language: - ko - en tags: - moe --- # **Synatra-Mixtral-8x7B** Synatra-Mixtral-8x7B **Synatra-Mixtral-8x7B** is a fine-tuned version of the Mixtral-8x7B-Instruct-v0.1 model using **Korean** datasets. This model features overwhelmingly superior comprehension and inference capabilities and is licensed under CC-BY-SA. # **EXL2 Info** [measurement.json](./measurement.json) 8.0bpw, 6.0bpw, 4.0bpw, 3.5bpw, 3.0bpw, 2.5bpw Measurement # **License** The "Model" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included **cc-by-sa-4.0** license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. # **Model Details** **Base Model** [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) **Trained On** A100 80GB * 6 **Instruction format** It follows **Alpaca** format. ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {input} ### Response: {output} ``` # **Model Benchmark** TBD # **Implementation Code** ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-Mixtral-8x7B") tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-Mixtral-8x7B") messages = [ {"role": "user", "content": "아인슈타인의 상대성이론에 대해서 자세히 설명해줘."}, ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` # **Author's Message** This model's training got sponsered by no one but support from people around Earth. [Support Me](https://www.buymeacoffee.com/mwell) Follow me on twitter: https://twitter.com/stablefluffy