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  1. app.py +0 -42
app.py CHANGED
@@ -86,48 +86,6 @@ def process_model(model_id, q_method, private_repo, oauth_token: gr.OAuthToken |
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  card.text = dedent(
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  f"""
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  # {new_repo_id}
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- This model was converted to GGUF format from [`{model_id}`](https://huggingface.co/{model_id}) 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/{model_id}) for more details on the model.
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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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-
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- ```bash
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- brew install llama.cpp
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-
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- ```
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- Invoke the llama.cpp server or the CLI.
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-
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- ### CLI:
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- ```bash
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- llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -p "The meaning to life and the universe is"
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- ```
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-
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- ### Server:
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- ```bash
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- llama-server --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
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- ```
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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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-
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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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-
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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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-
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- Step 3: Run inference through the main binary.
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- ```
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- ./llama-cli --hf-repo {new_repo_id} --hf-file {quantized_gguf_name} -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 {new_repo_id} --hf-file {quantized_gguf_name} -c 2048
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- ```
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  """
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  )
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  card.save(f"README.md")
 
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  card.text = dedent(
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  f"""
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  # {new_repo_id}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  """
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  )
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  card.save(f"README.md")