Reasoning-Distilled-ta-7B is based on the Qwen [KT] model, which was distilled by DeepSeek-AI/DeepSeek-R1-Distill-Qwen-7B. It has been fine-tuned on specialized datasets focusing on Tamil language-based reasoning tasks and chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving in the Tamil language, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks in Tamil.

Quickstart with Transformers

Here is a code snippet using apply_chat_template to show you how to load the tokenizer and model and generate content in Tamil:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Reasoning-Distilled-ta-7B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "பெரிய மொழி மாதிரிகள் பற்றி ஒரு சிறிய அறிமுகத்தை தரவும்."
messages = [
    {"role": "system", "content": "நீங்கள் DeepSeek-AI மூலம் உருவாக்கப்பட்ட Reasoning-Distilled-ta-7B. நீங்கள் ஒரு சக்திவாய்ந்த தமிழ் பகுத்தறிவு உதவியாளர்."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Intended Use:

Tamil Language Instruction-Following: The model excels in understanding and executing detailed instructions in Tamil, making it ideal for automation systems, virtual assistants, and educational tools tailored for Tamil-speaking users.
Tamil Text Generation: It can produce coherent, logically structured, and contextually relevant text in Tamil for use in content creation, summarization, and report writing.
Complex Reasoning Tasks in Tamil: With its fine-tuning for chain-of-thought reasoning, the model is well-suited for multi-step problem-solving, logical deduction, and question-answering tasks in Tamil.
Research and Development: It can support researchers and developers in exploring advancements in Tamil language processing, logical reasoning, and fine-tuning methodologies.
Educational Applications: The model can assist in teaching logical reasoning and problem-solving in Tamil by generating step-by-step solutions.

Limitations:

Domain-Specific Knowledge: While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains in Tamil.
Hallucination: Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data.
Bias in Training Data: The model's outputs may reflect biases present in the datasets it was fine-tuned on, which could limit its objectivity in certain contexts.
Performance on Non-Reasoning Tasks: The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses.
Resource-Intensive: Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.
Dependence on Input Quality: The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results.
Limited Multilingual Support: While optimized for Tamil, the model may not perform as well in other languages, especially those with significantly different linguistic structures.

This model is designed to empower Tamil-speaking users with advanced reasoning and text-generation capabilities, while also addressing the unique challenges of working with the Tamil language.