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ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M-GGUF overview

mrs83/FlowerTune Qwen2.5 Coder 0.5B Instruct Q4 K M GGUF WARNING This repository contains experimental models designed strictly for academic evaluation and res…

transformersggufcodellama-cppgguf-my-repotext-generationenbase_model:ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instructbase_model:quantized:ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instructlicense:mitendpoints_compatibleregion:usconversational

Runs locally from ~379.4 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation

Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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flowertune-qwen2.5-coder-0.5b-instruct-q4_k_m.ggufGGUFQ4_K_M379.4 MBDownload

Model Details

Model IDethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M-GGUF
Authorethicalabs
Pipelinetext-generation
Licensemit
Base modelethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct
Last modified2026-06-19T15:06:45.000Z

Model README

---

library_name: transformers

tags:

  • code
  • llama-cpp
  • gguf-my-repo

license: mit

language:

  • en

base_model: ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct

pipeline_tag: text-generation

---

mrs83/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M-GGUF

> [!WARNING]

> This repository contains experimental models designed strictly for academic evaluation and research purposes.

>

> Critical Constraints:

> * No Production Deployment: Experimental models must not be deployed in commercial, enterprise, or mission-critical environments under any circumstances.

> * No Liability: Experimental models are provided "as-is" without warranties of any kind. The developers assume zero liability for downstream consequences, system integration failures, or regulatory non-compliance resulting from unauthorized deployment.

This model was converted to GGUF format from ethicalabs/FlowerTune-Qwen2.5-Coder-0.5B-Instruct 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 mrs83/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M-GGUF --hf-file flowertune-qwen2.5-coder-0.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo mrs83/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M-GGUF --hf-file flowertune-qwen2.5-coder-0.5b-instruct-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 mrs83/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M-GGUF --hf-file flowertune-qwen2.5-coder-0.5b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo mrs83/FlowerTune-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M-GGUF --hf-file flowertune-qwen2.5-coder-0.5b-instruct-q4_k_m.gguf -c 2048

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