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--- |
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library_name: transformers |
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tags: |
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- llama-cpp |
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- gguf-my-repo |
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base_model: nbeerbower/Dumpling-Mistral-Nemo-8B |
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license: apache-2.0 |
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--- |
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# Triangle104/Dumpling-Mistral-Nemo-8B-Q4_K_S-GGUF |
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This model was converted to GGUF format from [`nbeerbower/Dumpling-Mistral-Nemo-8B`](https://huggingface.co/nbeerbower/Dumpling-Mistral-Nemo-8B) 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/nbeerbower/Dumpling-Mistral-Nemo-8B) for more details on the model. |
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--- |
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🧪 Experimental |
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An attempt to recover intelligence with a quick train, results are meh |
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Dumpling-Mistral-Nemo-8B |
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nbeerbower/mistral-nemo-kartoffel-PRUNE3 finetuned on: |
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-nbeerbower/GreatFirewall-DPO |
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-nbeerbower/Schule-DPO |
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-nbeerbower/Purpura-DPO |
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-nbeerbower/Arkhaios-DPO |
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-jondurbin/truthy-dpo-v0.1 |
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-antiven0m/physical-reasoning-dpo |
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-flammenai/Date-DPO-NoAsterisks |
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-flammenai/Prude-Phi3-DPO |
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-Atsunori/HelpSteer2-DPO (1,000 samples) |
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-jondurbin/gutenberg-dpo-v0.1 |
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-nbeerbower/gutenberg2-dpo |
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-nbeerbower/gutenberg-moderne-dpo. |
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Method |
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--- |
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QLoRA ORPO tune with 2x RTX 3090 for 2 epochs. |
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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/Dumpling-Mistral-Nemo-8B-Q4_K_S-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_s.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/Dumpling-Mistral-Nemo-8B-Q4_K_S-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_s.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/Dumpling-Mistral-Nemo-8B-Q4_K_S-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_s.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/Dumpling-Mistral-Nemo-8B-Q4_K_S-GGUF --hf-file dumpling-mistral-nemo-8b-q4_k_s.gguf -c 2048 |
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``` |
