aclava/Darwin-9B-Opus-Q8_0-GGUF overview
aclava/Darwin 9B Opus Q8 0 GGUF This model was converted to GGUF format from FINAL Bench/Darwin 9B Opus https://huggingface.co/FINAL Bench/Darwin 9B Opus using…
Runs locally from ~9.11 GB disk (12 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| darwin-9b-opus-q8_0.gguf | GGUF | Q8_0 | 9.11 GB | Download |
Model Details
| Model ID | aclava/Darwin-9B-Opus-Q8_0-GGUF |
|---|---|
| Author | aclava |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | FINAL-Bench/Darwin-9B-Opus |
| Last modified | 2026-08-28T11:07:49.000Z |
Model README
---
license: apache-2.0
tags:
- merge
- evolutionary-merge
- darwin
- darwin-v5
- model-mri
- reasoning
- advanced-reasoning
- chain-of-thought
- thinking
- qwen3.5
- qwen
- claude-opus
- distillation
- multilingual
- benchmark
- open-source
- apache-2.0
- layer-wise-merge
- coding-agent
- tool-calling
- long-context
- llama-cpp
- gguf-my-repo
language:
- en
- zh
- ko
- ja
- de
- fr
- es
- ru
- ar
- multilingual
pipeline_tag: text-generation
library_name: transformers
base_model: FINAL-Bench/Darwin-9B-Opus
model-index:
- name: Darwin-9B-Opus
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: GPQA Diamond
type: Idavidrein/gpqa
config: gpqa_diamond
metrics:
- type: accuracy
value: 82.5
name: Accuracy
---
aclava/Darwin-9B-Opus-Q8_0-GGUF
This model was converted to GGUF format from FINAL-Bench/Darwin-9B-Opus 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 aclava/Darwin-9B-Opus-Q8_0-GGUF --hf-file darwin-9b-opus-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo aclava/Darwin-9B-Opus-Q8_0-GGUF --hf-file darwin-9b-opus-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 aclava/Darwin-9B-Opus-Q8_0-GGUF --hf-file darwin-9b-opus-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo aclava/Darwin-9B-Opus-Q8_0-GGUF --hf-file darwin-9b-opus-q8_0.gguf -c 2048Run aclava/Darwin-9B-Opus-Q8_0-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models