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--- |
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license: other |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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base_model: AGI-0/Art-v0-3B |
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--- |
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# Triangle104/Art-v0-3B-Q6_K-GGUF |
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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. |
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Refer to the [original model card](https://huggingface.co/AGI-0/Art-v0-3B) for more details on the model. |
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--- |
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Model details: |
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- |
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Auto Regressive Thinker (Art) v0 3B |
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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 |
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Model Details |
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Base Model: Qwen2.5-3B-Instruct |
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Architecture: Transformer |
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Size: 3B parameters |
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Usage |
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The model incorporates a reasoning mechanism using specific tags: |
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<|start_reasoning|> model's reasoning process <|end_reasoning|> model's response |
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Recommendations |
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Use the model without quantization |
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Use the tokenizer chat template |
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Use a low temperature 0.1-0.3 and repetition_penalty of 1.1 |
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Training Details |
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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. |
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About Us |
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We are a community-funded AI research lab focused on advancing open-source AGI development. Our community members support us through Patreon donations. |
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Community Access |
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Our supporters get exclusive access to: |
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Training dataset |
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Training code and methodology |
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Behind-the-scenes development insights |
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Future model previews |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/Art-v0-3B-Q6_K-GGUF --hf-file art-v0-3b-q6_k.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/Art-v0-3B-Q6_K-GGUF --hf-file art-v0-3b-q6_k.gguf -c 2048 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/Art-v0-3B-Q6_K-GGUF --hf-file art-v0-3b-q6_k.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/Art-v0-3B-Q6_K-GGUF --hf-file art-v0-3b-q6_k.gguf -c 2048 |
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``` |
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