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readme : ggerganov -> ggml-org

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  1. README.md +7 -7
README.md CHANGED
@@ -9,7 +9,7 @@ tags:
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  base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
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  ---
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- # ggerganov/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
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  This model was converted to GGUF format from [`Qwen/Qwen3-Coder-30B-A3B-Instruct`](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) 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/Qwen/Qwen3-Coder-30B-A3B-Instruct) for more details on the model.
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@@ -24,19 +24,19 @@ 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 ggerganov/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-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 ggerganov/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-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).
@@ -46,9 +46,9 @@ cd llama.cpp && LLAMA_CURL=1 make
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  Step 3: Run inference through the main binary.
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  ```
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- ./llama-cli --hf-repo ggerganov/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-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 ggerganov/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q8_0.gguf -c 2048
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  ```
 
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  base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
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  ---
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+ # ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
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  This model was converted to GGUF format from [`Qwen/Qwen3-Coder-30B-A3B-Instruct`](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) 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/Qwen/Qwen3-Coder-30B-A3B-Instruct) for more details on the model.
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  ### CLI:
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  ```bash
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+ llama-cli --hf-repo ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-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 ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-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/ggml-org/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/ggml-org/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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  Step 3: Run inference through the main binary.
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  ```
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+ ./llama-cli --hf-repo ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-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 ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q8_0.gguf -c 2048
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  ```