---
license: creativeml-openrail-m
datasets:
- amphora/QwQ-LongCoT-130K
language:
- en
base_model: prithivMLmods/QwQ-LCoT-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- Long-CoT
- Qwen2.5
- 7B
- safetensors
- text-generation-inference
- QwQ
- SFT
- Math
- Qwen with Questions
- llama-cpp
- gguf-my-repo
---

# Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF
This model was converted to GGUF format from [`prithivMLmods/QwQ-LCoT-7B-Instruct`](https://huggingface.co/prithivMLmods/QwQ-LCoT-7B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/prithivMLmods/QwQ-LCoT-7B-Instruct) for more details on the model.

---
Model details:
-
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.

		Key Features:
	
Model Size:  

7.62B parameters (FP16 precision).

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)

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>).

Configuration Files:  

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

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

		Use Cases:
	
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.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_k_s.gguf -c 2048
```