---

language: ml
license: apache-2.0
tags:

- causal-lm
- assistant
- reasoning
- malayalam

---

# Dhee-Nxtgen-Qwen3-Malayalam-v1

## Model Description

Dheegpt-Qwen3-Malayalam is a large language model designed for high-quality natural language understanding and generation in **Malayalam**.
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 **Malayalam** 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-malayalam-v1"

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

# Regular conversation
prompt = "നമസ്കാരം! ഇന്നത്തെ കാലാവസ്ഥ എങ്ങനെയാണ്?"
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 **Malayalam**
* Story generation, narratives, and creative text
* Domain-specific natural language generation tasks in **Malayalam**

### 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 **Malayalam**; 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:

  ```bash
  pip install torch transformers vllm sentencepiece
  ```

Example vLLM command:

```bash
vllm serve \
  --model dheeyantra/dhee-nxtgen-qwen3-malayalam-v1 \
  --host 0.0.0.0 \
  --port 8000
```

---

## License

Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).