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MihaiPopa-1
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Model Details
Model Provider
MihaiPopa-1
Model Tree
Base
Qwen/Qwen3-0.6B
Adapter
this model
Input Modalities
Text
Output Modalities
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Supported Functionality
Dedicated Endpoints
Container
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch# 1. Load from your Hugging Face Repomodel_id = "MihaiPopa-1/Qwen3-0.6B-English-Hinglish-Preview-LoRA"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.float32, # Standard for CPUdevice_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))
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch# 1. Load from your Hugging Face Repomodel_id = "MihaiPopa-1/Qwen3-0.6B-English-Hinglish-Preview-LoRA"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.float32, # Standard for CPUdevice_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))
I used CMU's Hinglish Dog for this model (yes, really)!
This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.