An earlier variant of my English → Hinglish translation work: Llama 3 8B Instruct fine-tuned with QLoRA on a cleaned version of the CMU Hinglish DoG conversational dataset.

Looking for the recommended model? The newer variant trained on a curated news-domain corpus produces more fluent Hinglish: English_to_Hinglish_fintuned_lamma_3_8b_instruct.

Details

Base model: unsloth/llama-3-8b-Instruct-bnb-4bit
Method: QLoRA (4-bit) with Unsloth + HuggingFace TRL
Training data: suyash2739/Hinglish — cleaned from cmu_hinglish_dog (conversational domain)
License: Apache 2.0

How to use

Same interface as the main model:

python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="suyash2739/English_to_Hinglish_cmu_hinglish_dog",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

Prompt format: Translate the input from English to Hinglish to give the response. followed by ### Input: and ### Response: sections.

Why two variants?

This model captures conversational, dialogue-style Hinglish (CMU DoG is a document-grounded conversation dataset), while the main model targets news-register Hinglish. Comparing the two illustrates how strongly domain of the parallel corpus shapes code-mixing style in the output.

Limitations

Conversational-domain training data; formal text may translate awkwardly.
Romanized Hinglish only.
Inherits base-model and corpus biases.