84 lines
2.9 KiB
Markdown
84 lines
2.9 KiB
Markdown
---
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language: bn
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license: apache-2.0
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tags:
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- causal-lm
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- assistant
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- reasoning
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- bengali
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model_name: dhee-nxtgen-qwen3-bengali-v2
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library_name: transformers
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---
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# Dhee-NxtGen-Qwen3-Bengali-v2
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## Model Description
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**Dhee-NxtGen-Qwen3-Bengali-v2** is a large language model designed for natural and fluent Bengali (Bangla) understanding and generation.
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Built upon the **Qwen3** architecture, this model is optimized for **assistant-style dialogue**, **function-calling tasks**, and **reasoning-oriented responses**.
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It is part of DheeYantra’s multilingual initiative in collaboration with **NxtGen Cloud Technologies Private Limited**, focusing on building domain-adapted Indic LLMs.
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## Key Features
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- Fluent, context-aware Bengali text generation
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- Fine-tuned for assistant-style interactions and reasoning tasks
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- Handles open-domain question answering, summarization, and dialogue
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- Fully compatible with 🤗 Hugging Face Transformers
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- Optimized for VLLM serving for high-performance inference
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## Example Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "dheeyantra/dhee-nxtgen-qwen3-bengali-v2"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
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# Qwen3-compatible formatted prompt
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prompt = """<|im_start|>system
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You are a helpful assistant.<|im_end|>
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<|im_start|>user
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তুমি কি আমার জন্য একটি অ্যাপয়েন্টমেন্ট নির্ধারণ করতে পারবে?<|im_end|>
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<|im_start|>assistant
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=150)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Intended Uses & Limitations
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### Intended Uses
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- Bengali conversational chatbots and assistants
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- Function-calling and structured response generation
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- Story generation and summarization in Bengali
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- Natural dialogue systems for Indic AI applications
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### Limitations
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- May generate inaccurate or biased responses in rare cases
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- Performance can vary on out-of-domain or code-mixed inputs
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- Primarily optimized for Bengali; other languages may produce less fluent results
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## VLLM / High-Performance Serving Requirements
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For high-throughput serving with **vLLM**, ensure the following environment:
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- GPU with compute capability ≥ 8.0 (e.g., NVIDIA A100)
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- PyTorch 2.1+ and CUDA toolkit installed
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- For V100 GPUs (sm70), vLLM GPU inference is not supported; CPU fallback is possible but slower.
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Install dependencies:
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```bash
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pip install torch transformers vllm sentencepiece
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```
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Run vLLM server:
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```bash
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vllm serve --model dheeyantra/dhee-nxtgen-qwen3-bengali-v2 --host 0.0.0.0 --port 8000
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```
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## License
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Released under the **Apache 2.0 License**.
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---
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*Developed by DheeYantra in collaboration with NxtGen Cloud Technologies Pvt. Ltd.*
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