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Blackeye6941/Emotional-assistant_tinyllama-Q4_K_M-GGUF overview

Blackeye6941/Emotional assistant tinyllama Q4 K M GGUF This model was converted to GGUF format from Blackeye6941/Emotional assistant tinyllama https://huggingf…

ggufllama-cppgguf-my-repodataset:ShenLab/MentalChat16Kdataset:facebook/empathetic_dialoguesbase_model:Blackeye6941/Emotional-assistant_tinyllamabase_model:quantized:Blackeye6941/Emotional-assistant_tinyllamalicense:mitendpoints_compatibleregion:usconversational

Runs locally from ~636.9 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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tinyllama.ggufGGUFGGUF636.9 MBDownload

Model Details

Model IDBlackeye6941/Emotional-assistant_tinyllama-Q4_K_M-GGUF
AuthorBlackeye6941
Pipeline
Licensemit
Base modelBlackeye6941/Emotional-assistant_tinyllama
Last modified2026-06-30T09:21:23.000Z

Model README

---

license: mit

tags:

  • llama-cpp
  • gguf-my-repo

base_model: Blackeye6941/Emotional-assistant_tinyllama

datasets:

  • ShenLab/MentalChat16K
  • facebook/empathetic_dialogues

---

Blackeye6941/Emotional-assistant_tinyllama-Q4_K_M-GGUF

This model was converted to GGUF format from Blackeye6941/Emotional-assistant_tinyllama 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 Blackeye6941/Emotional-assistant_tinyllama-Q4_K_M-GGUF --hf-file emotional-assistant_tinyllama-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Blackeye6941/Emotional-assistant_tinyllama-Q4_K_M-GGUF --hf-file emotional-assistant_tinyllama-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 Blackeye6941/Emotional-assistant_tinyllama-Q4_K_M-GGUF --hf-file emotional-assistant_tinyllama-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Blackeye6941/Emotional-assistant_tinyllama-Q4_K_M-GGUF --hf-file emotional-assistant_tinyllama-q4_k_m.gguf -c 2048

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