Instructions to use prithivMLmods/Reasoning-Distilled-ta-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Reasoning-Distilled-ta-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prithivMLmods/Reasoning-Distilled-ta-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Reasoning-Distilled-ta-7B") model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Reasoning-Distilled-ta-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use prithivMLmods/Reasoning-Distilled-ta-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Reasoning-Distilled-ta-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Reasoning-Distilled-ta-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/prithivMLmods/Reasoning-Distilled-ta-7B
- SGLang
How to use prithivMLmods/Reasoning-Distilled-ta-7B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/Reasoning-Distilled-ta-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Reasoning-Distilled-ta-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/Reasoning-Distilled-ta-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Reasoning-Distilled-ta-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use prithivMLmods/Reasoning-Distilled-ta-7B with Docker Model Runner:
docker model run hf.co/prithivMLmods/Reasoning-Distilled-ta-7B
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:
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.
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