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---
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- llama-cpp
- gguf-my-repo
base_model: Qwen/Qwen3.8-27B
---
# serajfast4/Qwen3.8-27B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) 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/Qwen3.8-27B) for more details on the model.

## 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 serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.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 serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.gguf -c 2048
```

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	---
	library_name: transformers
	license: apache-2.0
	pipeline_tag: image-text-to-text
	tags:
	- llama-cpp
	- gguf-my-repo
	base_model: Qwen/Qwen3.8-27B
	---

	# serajfast4/Qwen3.8-27B-Q4_K_M-GGUF
	This model was converted to GGUF format from [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) 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/Qwen3.8-27B) for more details on the model.

	## 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 serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.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 serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo serajfast4/Qwen3.8-27B-Q4_K_M-GGUF --hf-file qwen3.8-27b-q4_k_m.gguf -c 2048
	```