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---
base_model: Qwen/QwQ-32B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/QWQ-32B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- llama-cpp
- gguf-my-repo
---
# Triangle104/QwQ-32B-Q5_K_S-GGUF
This model was converted to GGUF format from [`Qwen/QwQ-32B`](https://huggingface.co/Qwen/QwQ-32B) 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/Qwen/QwQ-32B) for more details on the model.
---
QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
This repo contains the QwQ 32B model, which has the following features:
-Type: Causal Language Models
-Training Stage: Pretraining & Post-training (Supervised Finetuning and Reinforcement Learning)
-Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
-Number of Parameters: 32.5B
-Number of Paramaters (Non-Embedding): 31.0B
-Number of Layers: 64
-Number of Attention Heads (GQA): 40 for Q and 8 for KV
-Context Length: Full 131,072 tokens
---
## 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-32B-Q5_K_S-GGUF --hf-file qwq-32b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-32B-Q5_K_S-GGUF --hf-file qwq-32b-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-32B-Q5_K_S-GGUF --hf-file qwq-32b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/QwQ-32B-Q5_K_S-GGUF --hf-file qwq-32b-q5_k_s.gguf -c 2048
```
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