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.
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.
- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
- 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.
- Long-context Support up to 128K tokens and can generate up to 8K tokens.
- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
Demo Start
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Deepthink-Reasoning-7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"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]
Run with Ollama [Ollama Run]
Ollama makes running machine learning models simple and efficient. Follow these steps to set up and run your GGUF models quickly.
Quick Start: Step-by-Step Guide
| Step | Description | Command / Instructions |
|---|---|---|
| 1 | Install Ollama 🦙 | Download Ollama from https://ollama.com/download and install it on your system. |
| 2 | Create Your Model File | - Create a file named after your model, e.g., metallama. |
| - Add the following line to specify the base model: | ||
| ```bash | ||
| FROM Llama-3.2-1B.F16.gguf | ||
| ``` | ||
| - Ensure the base model file is in the same directory. | ||
| 3 | Create and Patch the Model | Run the following commands to create and verify your model: |
| ```bash | ||
| ollama create metallama -f ./metallama | ||
| ollama list | ||
| ``` | ||
| 4 | Run the Model | Use the following command to start your model: |
| ```bash | ||
| ollama run metallama | ||
| ``` | ||
| 5 | Interact with the Model | Once the model is running, interact with it: |
| ```plaintext | ||
| >>> Tell me about Space X. | ||
| Space X, the private aerospace company founded by Elon Musk, is revolutionizing space exploration... | ||
| ``` |
Conclusion
With Ollama, running and interacting with models is seamless. Start experimenting today!