prithivMLmods/QwQ-4B-Instruct

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 /  / \  \ \ \/ \/ / /  / \  \    ______   /   |  |_ | __ \ 
/   \_/.  \ \     / /   \_/.  \  /_____/  /    ^   / | \_\ \
\_____\ \_/  \/\_/  \_____\ \_/           \____   |  |___  /
       \__>                \__>                |__|      \/ 

The QwQ-4B-Instruct is a lightweight and efficient fine-tuned language model for instruction-following tasks and reasoning. It is based on a quantized version of the Qwen2.5-7B model, optimized for inference speed and reduced memory consumption, while retaining robust capabilities for complex tasks.

With its robust natural language processing capabilities, QwQ-4B-Instruct 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/QwQ-4B-Instruct"

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!