metadata

language: vi
tags:
  - chatbot
  - vietnamese
  - conversational
license: mit
datasets:
  - your_dataset_name
metrics:
  - perplexity
  - bleu

Model Card for Vistral-7B-Chat

Model Details

Model Name: Vistral-7B-LegalBizAI
Version: 1.0
Model Type: Causal Language Model
Architecture: Transformer-based model with 7 billion parameters
Quantization: 8-bit quantized for efficiency

Usage

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "nhotin/vistral7B-legalbizai-q8-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Your text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))