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Arshad Bot Q4 K M GGUF

This model was converted to GGUF format from ArshadSWE/ArshadBot using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

TipoOtherModelo baseArshadSWE/Arshad_BotVersiónRevision f8b2e74Publicador@ArshadSWECcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
2,23 GB
Modelo base
ArshadSWE/Arshad_Bot
Versión
vRevision f8b2e74
Actualizado
2 oct 2026
Publicador
@ArshadSWE
Descargas
2
Fuente
Hugging Face

Descripción de la fuente

ArshadSWE/Arshad_Bot-Q4_K_M-GGUF

This model was converted to GGUF format from ArshadSWE/Arshad_Bot using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps 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 ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-q4_k_m.gguf -c 2048

Fuentes

1 fuente
Hugging FacePrincipalVersión f8b2e7462a987af9cbfbcb1433f2b52ccd4dfa7e
huggingface.co/ArshadSWE/Arshad_Bot-Q4_K_M-GGUF/tree/f8b2e7462a987af9cbfbcb1433f2b52ccd4dfa7e

Verificado 2 oct

Artefactos del modelo

1 artefacto

arshad_bot-q4_k_m.gguf

gguf · 2,23 GB · SHA-256 3fbf29561203…f9ae · Hugging Face

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Extractos de fuentes

3 extractos

--- base_model: ArshadSWE/Arshad_Bot tags: - llama-cpp - gguf-my-repo --- # ArshadSWE/Arshad_Bot-Q4_K_M-GGUF This model was converted to GGUF format from [`ArshadSWE/Arshad_Bot`](https://huggingface.co/ArshadSWE/Arshad_Bot) 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/ArshadSWE/Arshad_Bot) 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 ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-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 ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo ArshadSWE/Arshad_Bot-Q4_K_M-GGUF --hf-file arshad_bot-q4_k_m.gguf -c 2048 ```

Source context: 2 downloads · 0 likes · Repo ArshadSWE/Arshad_Bot-Q4_K_M-GGUF

Source context: 1 downloads · 0 likes · Repo ArshadSWE/Arshad_Bot-Q4_K_M-GGUF