82 lines
1.8 KiB
Markdown
82 lines
1.8 KiB
Markdown
---
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license: apache-2.0
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language:
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- ko
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- en
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tags:
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- moe
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---
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# **Synatra-Mixtral-8x7B**
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<img src="./Synatra-Mixtral.png" alt="Synatra-Mixtral-8x7B" width="512"/>
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**Synatra-Mixtral-8x7B** is a fine-tuned version of the Mixtral-8x7B-Instruct-v0.1 model using **Korean** datasets.
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This model features overwhelmingly superior comprehension and inference capabilities and is licensed under apache-2.0.
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# **Join Our Discord**
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[Server Link](https://discord.gg/MrBt3PXdXc)
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# **License**
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**OPEN**, Apache-2.0.
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# **Model Details**
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**Base Model**
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[mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
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**Trained On**
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A100 80GB * 6
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**Instruction format**
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It follows **Alpaca** format.
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{input}
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### Response:
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{output}
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```
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# **Model Benchmark**
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TBD
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# **Implementation Code**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("maywell/Synatra-Mixtral-8x7B")
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tokenizer = AutoTokenizer.from_pretrained("maywell/Synatra-Mixtral-8x7B")
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messages = [
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{"role": "user", "content": "아인슈타인의 상대성이론에 대해서 자세히 설명해줘."},
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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```
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# **Author's Message**
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This model's training got sponsered by no one but support from people around Earth.
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[Support Me](https://www.buymeacoffee.com/mwell)
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Contact Me on Discord - **is.maywell**
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Follow me on twitter: https://twitter.com/stablefluffy |