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h4bbo/FuseLLM-9B-Q4_K_M-GGUF overview

h4bbo/FuseLLM 9B Q4 K M GGUF This model was converted to GGUF format from h4bbo/FuseLLM 9B https://huggingface.co/h4bbo/FuseLLM 9B using llama.cpp via the ggml…

transformersggufcodeloracudahabbogame-server-emulationflashshockwavecontinued-pretrainingqwen3.5hybrid-attentiongated-deltanetllama-cppgguf-my-repotext-generationenbase_model:h4bbo/FuseLLM-9Bbase_model:adapter:h4bbo/FuseLLM-9Blicense:apache-2.0region:usconversational

Runs locally from ~5.24 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation
Author

Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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fusellm-9b-q4_k_m.ggufGGUFQ4_K_M5.24 GBDownload

Model Details

Model IDh4bbo/FuseLLM-9B-Q4_K_M-GGUF
Authorh4bbo
Pipelinetext-generation
Licenseapache-2.0
Base modelh4bbo/FuseLLM-9B
Last modified2026-07-14T22:42:28.000Z

Model README

---

library_name: transformers

base_model: h4bbo/FuseLLM-9B

tags:

  • code
  • lora
  • cuda
  • habbo
  • game-server-emulation
  • flash
  • shockwave
  • continued-pretraining
  • qwen3.5
  • hybrid-attention
  • gated-deltanet
  • llama-cpp
  • gguf-my-repo

language:

  • en

license: apache-2.0

inference: false

pipeline_tag: text-generation

---

h4bbo/FuseLLM-9B-Q4_K_M-GGUF

This model was converted to GGUF format from h4bbo/FuseLLM-9B 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 h4bbo/FuseLLM-9B-Q4_K_M-GGUF --hf-file fusellm-9b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo h4bbo/FuseLLM-9B-Q4_K_M-GGUF --hf-file fusellm-9b-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 h4bbo/FuseLLM-9B-Q4_K_M-GGUF --hf-file fusellm-9b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo h4bbo/FuseLLM-9B-Q4_K_M-GGUF --hf-file fusellm-9b-q4_k_m.gguf -c 2048

Run h4bbo/FuseLLM-9B-Q4_K_M-GGUF with guIDE

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