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Model: prithivMLmods/Reasoning-Distilled-ta-7B
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---
license: apache-2.0
language:
- ta
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen
- deepseek
- text-generation-inference
---
# **Reasoning-Distilled-ta-7B**
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:**
1. **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.
2. **Tamil Text Generation:** It can produce coherent, logically structured, and contextually relevant text in Tamil for use in content creation, summarization, and report writing.
3. **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.
4. **Research and Development:** It can support researchers and developers in exploring advancements in Tamil language processing, logical reasoning, and fine-tuning methodologies.
5. **Educational Applications:** The model can assist in teaching logical reasoning and problem-solving in Tamil by generating step-by-step solutions.
### **Limitations:**
1. **Domain-Specific Knowledge:** While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains in Tamil.
2. **Hallucination:** Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data.
3. **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.
4. **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.
5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.
6. **Dependence on Input Quality:** The models performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results.
7. **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.

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{
"_name_or_path": "prithivMLmods/Reasoning-Distilled-ta-7B",
"architectures": [
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],
"attention_dropout": 0.0,
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"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 131072,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": 151654,
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"use_mrope": false,
"use_sliding_window": false,
"vocab_size": 152064
}

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"single_word": false,
"special": false
},
"151658": {
"content": "</tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"bos_token": "<begin▁of▁sentence>",
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<User>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<Assistant><tool▁calls▁begin><tool▁call▁begin>' + tool['type'] + '<tool▁sep>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<tool▁call▁end>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<tool▁call▁begin>' + tool['type'] + '<tool▁sep>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<tool▁call▁end>'}}{{'<tool▁calls▁end><end▁of▁sentence>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<tool▁outputs▁end>' + message['content'] + '<end▁of▁sentence>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<Assistant>' + content + '<end▁of▁sentence>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<tool▁outputs▁begin><tool▁output▁begin>' + message['content'] + '<tool▁output▁end>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<tool▁output▁begin>' + message['content'] + '<tool▁output▁end>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<tool▁outputs▁end>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<Assistant>'}}{% endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<end▁of▁sentence>",
"extra_special_tokens": {},
"legacy": true,
"model_max_length": 131072,
"pad_token": "<|vision_pad|>",
"padding_side": "left",
"sp_model_kwargs": {},
"tokenizer_class": "LlamaTokenizer",
"unk_token": null,
"use_default_system_prompt": false
}