Synatra-Mixtral-8x7B-exl2

Synatra-Mixtral-8x7B (Original Weight)

<img src="./Synatra-Mixtral.png" alt="Synatra-Mixtral-8x7B" width="512"/>

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 apache-2.0.

EXL2 Info

measurement.json

8.0bpw, 6.0bpw, 4.0bpw, 3.5bpw, 3.0bpw, 2.6bpw, 2.3bpw

<img src="./measurement.png" alt="Measurement" width="512"/>

License

OPEN, Apache-2.0.

Model Details

Base Model
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

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

Contact Me on Discord - is.maywell

Follow me on twitter: https://twitter.com/stablefluffy