QwQ-LCoT-7B-Instruct Model in GGUF Format
3
7 commits
1 linked in READMEs
updated Dec 15, 2024
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 |

Model Size:
Model Sharding:
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)Tokenizer:
vocab.json (2.78 MB)merges.txt (1.82 MB)tokenizer.json (11.4 MB)special_tokens_map.json (e.g., <pad>, <eos>).Configuration Files:
config.json: Defines model architecture and hyperparameters.generation_config.json: Settings for inference and text generation tasks.Instruction Following:
Handle user instructions effectively, even for multi-step tasks.
Reasoning Tasks:
Perform logical reasoning and generate detailed step-by-step solutions.
Text Generation:
Generate coherent, context-aware responses.
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.
To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.
First, create a model file and name it appropriately. For example, you can name your model file metallama.
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.
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.
To run your newly created model, use the following command in your terminal:
ollama run metallama
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.
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.
QwQ-LCoT-7B-Instruct Model in GGUF Format
3
7 commits
1 linked in READMEs
updated Dec 15, 2024
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 |

Model Size:
Model Sharding:
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)Tokenizer:
vocab.json (2.78 MB)merges.txt (1.82 MB)tokenizer.json (11.4 MB)special_tokens_map.json (e.g., <pad>, <eos>).Configuration Files:
config.json: Defines model architecture and hyperparameters.generation_config.json: Settings for inference and text generation tasks.Instruction Following:
Handle user instructions effectively, even for multi-step tasks.
Reasoning Tasks:
Perform logical reasoning and generate detailed step-by-step solutions.
Text Generation:
Generate coherent, context-aware responses.
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.
To get started, download Ollama from https://ollama.com/download and install it on your Windows or Mac system.
First, create a model file and name it appropriately. For example, you can name your model file metallama.
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.
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.
To run your newly created model, use the following command in your terminal:
ollama run metallama
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.
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.