Triangle104/Art-v0-3B-Q5_K_M-GGUF

This model was converted to GGUF format from AGI-0/Art-v0-3B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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

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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 Triangle104/Art-v0-3B-Q5_K_M-GGUF --hf-file art-v0-3b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Art-v0-3B-Q5_K_M-GGUF --hf-file art-v0-3b-q5_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 Triangle104/Art-v0-3B-Q5_K_M-GGUF --hf-file art-v0-3b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Art-v0-3B-Q5_K_M-GGUF --hf-file art-v0-3b-q5_k_m.gguf -c 2048