language, license, tags, model_name, library_name
| language | license | tags | model_name | library_name | ||||
|---|---|---|---|---|---|---|---|---|
| bn | apache-2.0 |
|
dhee-nxtgen-qwen3-bengali-v2 | transformers |
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.