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:
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.