dheeyantra/dhee-nxtgen-qwen3-bengali-v2

Dhee-NxtGen-Qwen3-Bengali-v2

Model Description

Dhee-NxtGen-Qwen3-Bengali-v2 is a large language model designed for natural and fluent Bengali (Bangla) understanding and generation. Built upon the Qwen3 architecture, this model is optimized for assistant-style dialogue, function-calling tasks, and reasoning-oriented responses.

It is part of DheeYantra’s multilingual initiative in collaboration with NxtGen Cloud Technologies Private Limited, focusing on building domain-adapted Indic LLMs.

Key Features

• —Fluent, context-aware Bengali text generation
• —Fine-tuned for assistant-style interactions and reasoning tasks
• —Handles open-domain question answering, summarization, and dialogue
• —Fully compatible with 🤗 Hugging Face Transformers
• —Optimized for VLLM serving for high-performance inference

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

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

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

# Qwen3-compatible formatted prompt
prompt = """<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
তুমি কি আমার জন্য একটি অ্যাপয়েন্টমেন্ট নির্ধারণ করতে পারবে?<|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

• —Bengali conversational chatbots and assistants
• —Function-calling and structured response generation
• —Story generation and summarization in Bengali
• —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 Bengali; 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-bengali-v2   --host 0.0.0.0   --port 8000

License

Released under the Apache 2.0 License.

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