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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- nvidia |
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- AceInstruct |
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- code |
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- math |
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- general_domain |
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- instruct_model |
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- pytorch |
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- llama-cpp |
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- gguf-my-repo |
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base_model: nvidia/AceInstruct-7B |
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--- |
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# Triangle104/AceInstruct-7B-Q8_0-GGUF |
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This model was converted to GGUF format from [`nvidia/AceInstruct-7B`](https://huggingface.co/nvidia/AceInstruct-7B) 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/nvidia/AceInstruct-7B) for more details on the model. |
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--- |
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We introduce AceInstruct, a family of advanced SFT models for coding, |
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mathematics, and general-purpose tasks. The AceInstruct family, which |
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includes AceInstruct-1.5B, 7B, and 72B, is Improved using Qwen. |
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These models are fine-tuned on Qwen2.5-Base using general SFT datasets. These same datasets are also used in the training of AceMath-Instruct. |
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Different from AceMath-Instruct which is specialized for math |
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questions, AceInstruct is versatile and can be applied to a wide range |
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of domains. Benchmark evaluations across coding, mathematics, and |
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general knowledge tasks demonstrate that AceInstruct delivers |
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performance comparable to Qwen2.5-Instruct. |
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For more information about AceInstruct, check our website and paper. |
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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/AceInstruct-7B-Q8_0-GGUF --hf-file aceinstruct-7b-q8_0.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/AceInstruct-7B-Q8_0-GGUF --hf-file aceinstruct-7b-q8_0.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/AceInstruct-7B-Q8_0-GGUF --hf-file aceinstruct-7b-q8_0.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/AceInstruct-7B-Q8_0-GGUF --hf-file aceinstruct-7b-q8_0.gguf -c 2048 |
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``` |
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