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Model: prithivMLmods/Deepthink-Reasoning-14B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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datasets:
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- prithivMLmods/Math-Solve
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- AI-MO/NuminaMath-CoT
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- amphora/QwQ-LongCoT-130K
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- amphora/QwQ-LongCoT-130K-2
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-14B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- Math
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- text-generation-inference
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- Deep-think
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---
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# **Deepthink-Reasoning-14B**
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The *Deepthink-Reasoning-14B* model is a fine-tuned version of the *Qwen2.5* base model, designed for text generation tasks requiring deep reasoning, logical structuring, and problem-solving. This model leverages its optimized architecture to provide accurate and contextually relevant outputs for complex queries, making it ideal for applications in education, programming, and creative writing.
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With its robust natural language processing capabilities, *Deepthink-Reasoning-14B* excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates an advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs.
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- It possesses significantly **more knowledge** and exhibits greatly improved capabilities in **coding** and **mathematics**, thanks to specialized expert models in these domains.
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- Offers substantial improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g., tables), and **producing structured outputs**, especially in JSON format. It is **more resilient to diverse system prompts**, enhancing role-play implementation and condition-setting for chatbots.
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- Provides **long-context support** for up to 128K tokens and can generate up to 8K tokens.
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- Features **multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
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# **Quickstart with Tranformers**
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Deepthink-Reasoning-14B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Give me a short introduction to large language model."
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messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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### **Intended Use:**
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1. **Education:** Ideal for creating step-by-step solutions to complex problems, explanations, and generating educational content in multiple languages.
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2. **Programming:** Excels in coding tasks, debugging, and generating structured outputs such as JSON, enhancing productivity for developers.
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3. **Creative Writing:** Suitable for generating stories, essays, and other forms of creative content with logical and coherent structure.
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4. **Long-Context Processing:** Capable of handling and generating long texts, making it useful for summarizing lengthy documents or creating detailed reports.
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5. **Multilingual Applications:** Supports 29+ languages, enabling usage in global contexts for translation, multilingual education, and cross-cultural communication.
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6. **Data Structuring:** Performs well with structured data, such as tables and JSON outputs, making it effective for business analytics and automated report generation.
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7. **Chatbots and Role-Play:** Enhances chatbot interactions with its ability to follow diverse instructions, adapt to different prompts, and maintain long conversational contexts.
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### **Limitations:**
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1. **Resource Requirements:** Its large size and capabilities demand significant computational resources, making it less accessible for low-resource environments.
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2. **Hallucination Risk:** The model may generate incorrect or fabricated information, particularly when dealing with unknown or ambiguous inputs.
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3. **Limited Domain-Specific Expertise:** While it has broad knowledge, it might underperform in highly specialized fields not covered in its training data.
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4. **Long-Context Limitations:** Although it supports up to 128K tokens, performance may degrade or exhibit inefficiencies with extremely lengthy or complex contexts.
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5. **Bias in Outputs:** The model might reflect biases present in its training data, affecting its objectivity in certain contexts or cultural sensitivity in multilingual outputs.
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6. **Dependence on Prompt Quality:** Results heavily depend on well-structured and clear inputs. Poorly framed prompts can lead to irrelevant or suboptimal responses.
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7. **Error in Multilingual Output:** Despite robust multilingual support, subtle errors in grammar, syntax, or cultural nuances might appear, especially in low-resource languages.
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