metadata

language:
  - en
  - hi
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
  - Hinglish
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
datasets:
  - cmu_hinglish_dog
  - suyash2739/Hinglish

Better model

I have just deployed a better model than this on [https://huggingface.co/suyash2739/English_to_Hinglish_fintuned_lamma_3_8b_instruct ]

Loss Curve

Evaluation Loss

Colab Files:

Model_Use.ipynb file to use the model
Hinglish_train_lamma_3_8b_instruct_.ipynb to see how the model is trained

Inference:

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

from unsloth import FastLanguageModel
import torch
max_seq_length = 2048
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 = "suyash2739/English_to_Hinglish_cmu_hinglish_dog",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

prompt = """Translate the input from English to Hinglish to give the response.

### Input:
{}

### Response:
{}"""


inputs = tokenizer(
[
  prompt.format(
        """This is a fine-tuned Hinglish translation model using Llama 3.""", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)

_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
## ye ek fine-tuned Hinglish translation model hai jisme Llama 3 use kiya gaya hai

Uploaded model

Developed by: suyash2739
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit

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