MihaiPopa-1/Qwen3-0.6B-English-Hinglish-Preview

Qwen 3 0.6B English <-> Hinglish (Preview)

This is just a English to Hinglish (code-switched English-Hindi) and back transaltion model. Fine-tuned from Qwen 3 0.6B (with Unsloth), this model is designed for translation tasks on any device!

Issues

Low BLEU Scores: Because I trained this for ONLY a epoch, BLEU scores are lower than in the final version. We're going to train a new version for 5 epochs!

Notes

This is still a experimental model and shouldn't be used for tasks where accurate translations matter!

Evaluation Results

Direction	BLEU (250 sentences from test split)
English -> Hinglish	4.94
Hinglish -> English	17.89

Usage

Code is by Gemini 3 Flash (then some little modifications by myself):

English to Hinglish

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 1. Load from your Hugging Face Repo
model_id = "MihaiPopa-1/Qwen3-0.6B-English-Hinglish-Preview"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32, # Standard for CPU
    device_map="cpu"           # Forces CPU usage
)

# 2. Translate (replace ron_Latn with your language here)
prompt = "<|im_start|>user\nTranslate English to Hinglish: Hello, how are you doing?<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
    
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Hinglish to English

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 1. Load from your Hugging Face Repo
model_id = "MihaiPopa-1/Qwen3-0.6B-English-Hinglish-Preview"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32, # Standard for CPU
    device_map="cpu"           # Forces CPU usage
)

# 2. Translate (replace ron_Latn with your language here)
prompt = "<|im_start|>user\nTranslate Hinglish to English: Hello, tum kaise ho?<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
    
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Data Used

I used CMU's Hinglish Dog for this model (yes, really)!

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Uploaded finetuned model

• Developed by: MihaiPopa-1
• License: apache-2.0
• Finetuned from model : unsloth/qwen3-0.6b-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.