66 lines
1.9 KiB
Markdown
66 lines
1.9 KiB
Markdown
---
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base_model: KBTG-Labs/THaLLE-0.1-7B-fa
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language:
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- finance
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- llama-cpp
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---
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# KBTG-Labs/THaLLE-0.1-7B-fa-GGUF
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This model was converted to GGUF format from [`KBTG-Labs/THaLLE-0.1-7B-fa`](https://huggingface.co/KBTG-Labs/THaLLE-0.1-7B-fa) using llama.cpp.
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Refer to the [original model card](https://huggingface.co/KBTG-Labs/THaLLE-0.1-7B-fa) 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 with your perfered quantization level ("q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k", "q8_0", "f16").
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Smaller quantization is faster and use less memory, but will be less accurate.
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### CLI:
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```bash
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llama-cli --hf-repo KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.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 KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.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 KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.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 KBTG-Labs/THaLLE-0.1-7B-fa-GGUF --hf-file thalle-0.1-7b-fa-<QUANTIZATION_LEVEL>.gguf -c 2048
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```
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