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dheeyantra/
dhee-nxtgen-qwen3-hindi-v1

Name: dhee-nxtgen-qwen3-hindi-v1
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Dhee-Nxtgen-Qwen3-Hindi-v1

Model Description

Dheegpt-Qwen3-Hindi is a large language model designed for high-quality natural language understanding and generation in Hindi. It is based on the Qwen3 architecture and optimized for both dialogue and reasoning tasks.

The model supports fluent conversational responses and reasoning-style outputs, making it suitable for applications like chatbots, virtual assistants, and step-by-step question answering.

Key Features

Fluent and context-aware Hindi text generation
Optimized for assistant-style conversations
Handles summarization, question answering, and open-ended text generation
Fully compatible with Hugging Face transformers
Supports integration with VLLM for high-performance batched inference

Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "dheeyantra/dhee-nxtgen-qwen3-hindi-v1"

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

# Regular conversation
prompt = "नमस्कार! आज का मौसम कैसा है?"
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

Chatbots and conversational agents in Hindi
Story generation, narratives, and creative text
Domain-specific natural language generation tasks in Hindi

Limitations

Model outputs are based on the patterns and information in training data; it may generate incorrect or biased information.
Performance may vary depending on input complexity.
Primarily designed for Hindi; code-mixed or other languages may yield lower quality outputs.
May produce irrelevant or hallucinated content occasionally.

VLLM / High-Performance Serving Requirements

To serve this model using vLLM, ensure the following:

GPU with compute capability ≥ 8.0 (e.g., NVIDIA A100).
PyTorch 2.1+ with CUDA toolkit installed.
For Tesla V100 (sm70), vLLM GPU inference is not supported; CPU-only fallback is possible but slow.
Python dependencies:

pip install torch transformers vllm sentencepiece

Example vLLM command:

bash

vllm serve \
  --model dheeyantra/dhee-nxtgen-qwen3-hindi-v1 \
  --host 0.0.0.0 \
  --port 8000

License

Released under the Apache 2.0 License.