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---
license: llama3
---
# Model
fine-tuned LLaMA 3 8B on synthetic dataset generated by GPT-4 and LLaMA 3 70B via MLX-LM
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "mzbac/llama-3-8B-grammar-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{
"role": "system",
"content": "Please correct, polish, or translate the text to standard English.",
},
]
messages.append({"role": "user", "content":"Text=```neither 经理或员工 has been informed about the meeting```"})
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.1,
)
response = outputs[0]
print(tokenizer.decode(response))
# <|begin_of_text|><|start_header_id|>system<|end_header_id|>
# Please correct, polish, or translate the text to standard English.<|eot_id|><|start_header_id|>user<|end_header_id|>
# Text=```neither 经理或员工 has been informed about the meeting```<|eot_id|><|start_header_id|>assistant<|end_header_id|>
# Output=Neither the manager nor the employees have been informed about the meeting.<|eot_id|>
``` |