Hinglish-Project/llama-3-8b-English-to-Hinglish

Inference:

!pip install -q "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install -q --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes

from unsloth import FastLanguageModel
import torch
max_seq_length = 512
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Hinglish-Project/llama-3-8b-English-to-Hinglish",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

def pipe(prompt):
  alpaca_prompt = """### Instrucion: Translate given text to Hinglish Text:

### Input:
{}

### Response:
"""

  inputs = tokenizer(
      [
          alpaca_prompt.format(prompt),
      ], return_tensors = "pt").to("cuda")

  outputs = model.generate(**inputs, max_new_tokens = 2048, use_cache = True)
  raw_text = tokenizer.batch_decode(outputs)[0]
  return raw_text.split("### Response:\n")[1].split("<|end_of_text|>")[0]

text = "This is a fine-tuned Hinglish translation model using Llama 3."
pipe(text)
## yeh ek fine-tuned Hinglish translation model hai jisme Llama 3 ka use kiya gaya hai.

Uploaded model

• Developed by: Hinglish-Project
• License: apache-2.0
• Finetuned from model : unsloth/llama-8b-bnb-4bit

This Llama3 model was trained 2x faster with Unsloth and Huggingface's TRL library.