68 lines
1.8 KiB
Markdown
68 lines
1.8 KiB
Markdown
---
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language:
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- it
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- en
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license: apache-2.0
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tags:
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- text-generation
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- causal-lm
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- bilingual
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- italian
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- english
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- small-language-model
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- trained-from-scratch
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- quark
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- instruct
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- sft
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- chat
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- llama-cpp
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- gguf-my-repo
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library_name: transformers
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pipeline_tag: text-generation
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base_model: ThingAI/Quark-270m-Instruct
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---
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# usermma/Quark-270m-Instruct-GGUF
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This model was converted to GGUF format from [`ThingAI/Quark-270m-Instruct`](https://huggingface.co/ThingAI/Quark-270m-Instruct) using llama.cpp.
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Refer to the [original model card](https://huggingface.co/ThingAI/Quark-270m-Instruct) for more details on the model.
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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 usermma/Quark-270m-Instruct-GGUF --hf-file quark-270m-instruct-q2_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 usermma/Quark-270m-Instruct-GGUF --hf-file quark-270m-instruct-q2_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 usermma/Quark-270m-Instruct-GGUF --hf-file quark-270m-instruct-q2_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 usermma/Quark-270m-Instruct-GGUF --hf-file quark-270m-instruct-q2_k.gguf -c 2048
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```
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