Dhee-NxtGen-Qwen3-Gujarati-v2 is a large language model designed for natural Gujarati 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 Gujarati conversational systems and reasoning agents.

Key Features

Fluent and context-aware Gujarati 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

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "dheeyantra/dhee-nxtgen-qwen3-gujarati-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
શું તમે મારી માટે એક અપોઇન્ટમેન્ટ શેડ્યૂલ કરી શકો છો?<|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

Gujarati conversational chatbots and assistants
Function-calling and structured response generation
Story generation and summarization in Gujarati
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 Gujarati; 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-gujarati-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.