dhee-nxtgen-qwen3-maithili-v2

Dhee-NxtGen-Qwen3-Maithili-v2

Model Description

Dhee-NxtGen-Qwen3-Maithili-v2 is a large language model designed for natural Maithili language understanding and generation.
It is based on the Qwen3 architecture and fine-tuned for assistant-style, function-calling, and reasoning-based conversational tasks.

Developed by DheeYantra in collaboration with NxtGen Cloud Technologies Pvt. Ltd., this model powers intelligent Maithili conversational systems and reasoning agents.

Key Features

• Fluent and context-aware Maithili text generation
• Optimized for assistant and reasoning conversations
• Supports open-ended generation, summarization, and dialogue
• Fully compatible with 🤗 Hugging Face Transformers
• Optimized for VLLM for high-performance inference

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "dheeyantra/dhee-nxtgen-qwen3-maithili-v2"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

# Example prompt
prompt = """<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
की अहाँ हमरा एकटा अपॉइन्टमेंट सिड्यूल करी देब?<|im_end|>
<|im_start|>assistant
"""


inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Uses & Limitations

Intended Uses

• Maithili conversational chatbots and assistants
• Function-calling and structured response generation
• Story generation and summarization in Maithili
• Natural dialogue systems for Indic AI applications

Limitations

• May generate inaccurate or biased responses in rare cases
• Performance can vary on out-of-domain or code-mixed inputs
• Primarily optimized for Maithili; other languages may produce less fluent results

VLLM / High-Performance Serving Requirements

For high-throughput serving with vLLM, ensure the following environment:

• GPU with compute capability ≥ 8.0 (e.g., NVIDIA A100)
• PyTorch 2.1+ and CUDA toolkit installed
• For V100 GPUs (sm70), vLLM GPU inference is not supported; CPU fallback is possible but slower.

Install dependencies:

pip install torch transformers vllm sentencepiece

Run vLLM server:

vllm serve   --model dheeyantra/dhee-nxtgen-qwen3-maithili-v2   --host 0.0.0.0   --port 8000

License

Released under the Apache 2.0 License.

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Developed by DheeYantra in collaboration with NxtGen Cloud Technologies Pvt. Ltd.