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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.

TypeOtherModèle de baseArshadSWE/Arshad_BotVersionRevision f8b2e74Éditeur@ArshadSWECClasse

Source du modèle

Type
Other
Taille du fichier
2,23 GB
Modèle de base
ArshadSWE/Arshad_Bot
Version
vRevision f8b2e74
Mis à jour
2 oct. 2026
Éditeur
@ArshadSWE
Téléchargements
2
Source
Hugging Face

Description de la source

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

Sources

1 source
Hugging FacePrincipalVersion f8b2e7462a987af9cbfbcb1433f2b52ccd4dfa7e
huggingface.co/ArshadSWE/Arshad_Bot-Q4_K_M-GGUF/tree/f8b2e7462a987af9cbfbcb1433f2b52ccd4dfa7e

Vérifié 2 oct.

Artefacts du modèle

1 artefact

arshad_bot-q4_k_m.gguf

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

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Extraits de sources

3 extraits

--- 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