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Lev384501/qwen3-0.6b-russian-dialogues-Q8_0-GGUF overview

Lev384501/qwen3 0.6b russian dialogues Q8 0 GGUF This model was converted to GGUF format from Lev384501/qwen3 0.6b russian dialogues https://huggingface.co/Lev…

transformersggufllama-cppgguf-my-reporudataset:Den4ikAI/russian_dialoguesbase_model:Lev384501/qwen3-0.6b-russian-dialoguesbase_model:quantized:Lev384501/qwen3-0.6b-russian-dialogueslicense:apache-2.0endpoints_compatibleregion:us

Runs locally from ~609.8 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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1 GGUF files detected
Direct downloads for local inference
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qwen3-0.6b-russian-dialogues-q8_0.ggufGGUFQ8_0609.8 MBDownload

Model Details

Model IDLev384501/qwen3-0.6b-russian-dialogues-Q8_0-GGUF
AuthorLev384501
Pipeline
Licenseapache-2.0
Base modelLev384501/qwen3-0.6b-russian-dialogues
Last modified2026-07-09T20:06:30.000Z

Model README

---

license: apache-2.0

datasets:

  • Den4ikAI/russian_dialogues

language:

  • ru

base_model: Lev384501/qwen3-0.6b-russian-dialogues

library_name: transformers

tags:

  • llama-cpp
  • gguf-my-repo

---

Lev384501/qwen3-0.6b-russian-dialogues-Q8_0-GGUF

This model was converted to GGUF format from Lev384501/qwen3-0.6b-russian-dialogues 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 Lev384501/qwen3-0.6b-russian-dialogues-Q8_0-GGUF --hf-file qwen3-0.6b-russian-dialogues-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Lev384501/qwen3-0.6b-russian-dialogues-Q8_0-GGUF --hf-file qwen3-0.6b-russian-dialogues-q8_0.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 Lev384501/qwen3-0.6b-russian-dialogues-Q8_0-GGUF --hf-file qwen3-0.6b-russian-dialogues-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Lev384501/qwen3-0.6b-russian-dialogues-Q8_0-GGUF --hf-file qwen3-0.6b-russian-dialogues-q8_0.gguf -c 2048

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