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---
license: other
tags:
- llama-cpp
- gguf-my-repo
base_model: AGI-0/Art-v0-3B
---
# Triangle104/Art-v0-3B-Q4_K_M-GGUF
This model was converted to GGUF format from [`AGI-0/Art-v0-3B`](https://huggingface.co/AGI-0/Art-v0-3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/AGI-0/Art-v0-3B) for more details on the model.
---
Model details:
-
Auto Regressive Thinker (Art) v0 3B
Art v0 3B is our inaugural model in the Art series, fine-tuned from Qwen/Qwen2.5-3B-Instruct using a specialized dataset generated with Gemini 2.0 Flash Thinking. Read more about the Art series
Model Details
Base Model: Qwen2.5-3B-Instruct
Architecture: Transformer
Size: 3B parameters
Usage
The model incorporates a reasoning mechanism using specific tags:
<|start_reasoning|> model's reasoning process <|end_reasoning|> model's response
Recommendations
Use the model without quantization
Use the tokenizer chat template
Use a low temperature 0.1-0.3 and repetition_penalty of 1.1
Training Details
This experimental model was trained on a curated dataset generated using Gemini 2.0 Flash Thinking. Detailed training methodology, dataset, and code are available exclusively to our community members.
About Us
We are a community-funded AI research lab focused on advancing open-source AGI development. Our community members support us through Patreon donations.
Community Access
Our supporters get exclusive access to:
Training dataset
Training code and methodology
Behind-the-scenes development insights
Future model previews
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Art-v0-3B-Q4_K_M-GGUF --hf-file art-v0-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Art-v0-3B-Q4_K_M-GGUF --hf-file art-v0-3b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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 Triangle104/Art-v0-3B-Q4_K_M-GGUF --hf-file art-v0-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Art-v0-3B-Q4_K_M-GGUF --hf-file art-v0-3b-q4_k_m.gguf -c 2048
```
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