prithivMLmods/QwQ-LCoT-7B-Instruct-GGUF

QwQ-LCoT-7B-Instruct Model in GGUF Format

The QwQ-LCoT-7B-Instruct is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on the amphora/QwQ-LongCoT-130K dataset, focusing on chain-of-thought (CoT) reasoning.

File Name	Size	Description	Upload Status
.gitattributes	1.79 kB	Tracks files stored with Git LFS.	Uploaded
QwQ-LCoT-7B-Instruct.F16.gguf	15.2 GB	Full-precision model file in GGUF format.	Uploaded (LFS)
QwQ-LCoT-7B-Instruct.Q4_K_M.gguf	4.68 GB	Quantized model weights (Q4_K_M precision).	Uploaded (LFS)
QwQ-LCoT-7B-Instruct.Q5_K_M.gguf	5.44 GB	Quantized model weights (Q5_K_M precision).	Uploaded (LFS)
QwQ-LCoT-7B-Instruct.Q8_0.gguf	8.1 GB	Quantized model weights (Q8_0 precision).	Uploaded (LFS)
README.md	42 Bytes	Initial commit for project documentation.	Uploaded
config.json	29 Bytes	Configuration file for model setup.	Uploaded

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Sample Long CoT:

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Key Features:

1. Model Size:

   • 7.62B parameters (FP16 precision).

2. Model Sharding:

   • The model weights are split into 4 shards (safetensors) for efficient storage and download:
     • model-00001-of-00004.safetensors (4.88 GB)
     • model-00002-of-00004.safetensors (4.93 GB)
     • model-00003-of-00004.safetensors (4.33 GB)
     • model-00004-of-00004.safetensors (1.09 GB)

3. Tokenizer:

   • Byte-pair encoding (BPE) based.
   • Files included:
     • vocab.json (2.78 MB)
     • merges.txt (1.82 MB)
     • tokenizer.json (11.4 MB)
   • Special tokens mapped in special_tokens_map.json (e.g., <pad>, <eos>).

4. Configuration Files:

   • config.json: Defines model architecture and hyperparameters.
   • generation_config.json: Settings for inference and text generation tasks.

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Training Dataset:

• Dataset Name: amphora/QwQ-LongCoT-130K
• Size: 133k examples.
• Focus: Chain-of-Thought reasoning for complex tasks.

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Use Cases:

1. Instruction Following:
   Handle user instructions effectively, even for multi-step tasks.

2. Reasoning Tasks:
   Perform logical reasoning and generate detailed step-by-step solutions.

3. Text Generation:
   Generate coherent, context-aware responses.

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Run with Ollama [ Ollama Run ]

Overview

Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes.

Table of Contents

• Download and Install Ollama
• Steps to Run GGUF Models
  • 1. Create the Model File
  • 2. Add the Template Command
  • 3. Create and Patch the Model
• Running the Model
• Sample Usage

Download and Install Ollama🦙

To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.

Steps to Run GGUF Models

1. Create the Model File

First, create a model file and name it appropriately. For example, you can name your model file metallama.

2. Add the Template Command

In your model file, include a FROM line that specifies the base model file you want to use. For instance:

FROM Llama-3.2-1B.F16.gguf

Ensure that the model file is in the same directory as your script.

3. Create and Patch the Model

Open your terminal and run the following command to create and patch your model:

ollama create metallama -f ./metallama

Once the process is successful, you will see a confirmation message.

To verify that the model was created successfully, you can list all models with:

ollama list

Make sure that metallama appears in the list of models.

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Running the Model

To run your newly created model, use the following command in your terminal:

ollama run metallama

Sample Usage / Test

In the command prompt, you can execute:

D:\>ollama run metallama

You can interact with the model like this:

>>> write a mini passage about space x
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
plays a pivotal role in pushing the boundaries of human exploration and settlement.

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Conclusion

With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient.

• This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include.

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