Dhee-NxtGen-Qwen3-Telugu-v2

Model Description

Dhee-NxtGen-Qwen3-Telugu-v2 is a large language model developed by DheeYantra in collaboration with NxtGen Cloud Technologies Pvt. Ltd.
It is based on the Qwen3 architecture and fine-tuned for assistant-style, function-calling, and reasoning-based conversational tasks in Telugu.

This model is capable of producing fluent, natural, and contextually coherent Telugu text, suitable for building intelligent assistants and domain-specific dialogue systems.

Key Features

Fluent and context-aware Telugu text generation
Optimized for reasoning and assistant-style conversations
Supports summarization, question answering, and creative writing
Fully compatible with 🤗 Hugging Face Transformers
Works seamlessly with VLLM for high-performance inference

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "dheeyantra/dhee-nxtgen-qwen3-telugu-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
మీరు నా కోసం ఒక అపాయింట్‌మెంట్ షెడ్యూల్ చేయగలరా?<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Uses & Limitations

Intended Uses

Telugu conversational chatbots and assistants
Function-calling and structured response generation
Story generation and summarization in Telugu
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 Telugu; 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-telugu-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.

dheeyantra/dhee-nxtgen-qwen3-telugu-v2