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--- |
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license: creativeml-openrail-m |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-7B-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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- code-solve |
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- algorithm |
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- codepy |
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- qwen_base |
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- 7b |
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- CoT |
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- deep-think |
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--- |
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<pre align="center"> |
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.___ __ .__ .__ __ ____________. |
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__| _/____ ____ _______/ |_| |__ |__| ____ | | __ \______ \_ |__ |
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/ __ |/ __ \_/ __ \\____ \ __\ | \| |/ \| |/ / / /| __ \ |
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/ /_/ \ ___/\ ___/| |_> > | | Y \ | | \ < / / | \_\ \ |
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\____ |\___ >\___ > __/|__| |___| /__|___| /__|_ \ /____/ |___ / |
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\/ \/ \/|__| \/ \/ \/ \/ |
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</pre> |
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The **Deepthink-Reasoning-7B** is a fine-tuned version of the **Qwen2.5-7B-Instruct** base model, designed for text generation tasks that require 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-7B** excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs. |
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- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains. |
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- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots. |
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- **Long-context Support** up to 128K tokens and can generate up to 8K tokens. |
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- **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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# **Demo Start** |
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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-7B" |
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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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# **Run with Ollama [Ollama Run]** |
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Ollama makes running machine learning models simple and efficient. Follow these steps to set up and run your GGUF models quickly. |
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## Quick Start: Step-by-Step Guide |
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| Step | Description | Command / Instructions | |
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|------|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| 1 | **Install Ollama 🦙** | Download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your system. | |
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| 2 | **Create Your Model File** | - Create a file named after your model, e.g., `metallama`. | |
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| | | - Add the following line to specify the base model: | |
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| | | ```bash | |
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| | | FROM Llama-3.2-1B.F16.gguf | |
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| | | ``` | |
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| | | - Ensure the base model file is in the same directory. | |
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| 3 | **Create and Patch the Model** | Run the following commands to create and verify your model: | |
|
| | | ```bash | |
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| | | ollama create metallama -f ./metallama | |
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| | | ollama list | |
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| | | ``` | |
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| 4 | **Run the Model** | Use the following command to start your model: | |
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| | | ```bash | |
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| | | ollama run metallama | |
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| | | ``` | |
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| 5 | **Interact with the Model** | Once the model is running, interact with it: | |
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| | | ```plaintext | |
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| | | >>> Tell me about Space X. | |
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| | | Space X, the private aerospace company founded by Elon Musk, is revolutionizing space exploration... | |
|
| | | ``` | |
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|
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## Conclusion |
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With Ollama, running and interacting with models is seamless. Start experimenting today! |
