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--- |
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base_model: nbeerbower/Xiaolong-Qwen3-0.6B |
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datasets: |
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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 |
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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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- GeneralReasoning/GeneralThought-430K |
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- nvidia/OpenMathReasoning |
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- nvidia/OpenCodeReasoning |
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library_name: transformers |
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license: apache-2.0 |
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tags: |
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- orpo |
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- uncensored |
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- reasoning |
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- cot |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/Xiaolong-Qwen3-0.6B-Q5_K_M-GGUF |
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This model was converted to GGUF format from [`nbeerbower/Xiaolong-Qwen3-0.6B`](https://huggingface.co/nbeerbower/Xiaolong-Qwen3-0.6B) 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/Xiaolong-Qwen3-0.6B) for more details on the model. |
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--- |
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Xiaolong is a small, uncensored, reasoning-focused model finetuned using ORPO and QLoRA on top of Qwen3-0.6B-abliterated-TIES. |
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Finetuning Details |
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- |
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- Method: ORPO |
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- Epochs: 1.3 |
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- Learning Rate: 5e-6, cosine decay w/ 5% warmup |
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- Batch Size: 4 x 8 (32 effective) |
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- Max Grad Norm: 0.3 |
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- LoRA Rank: 64 |
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- Hardware: 1x NVIDIA RTX A6000 |
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Dataset Composition |
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- |
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~9,100 samples. 3,000 used Chain of Thought reasoning. |
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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 (1000 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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Chain of Thought |
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- |
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- GeneralReasoning/GeneralThought-430K (1000 samples) |
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- nvidia/OpenMathReasoning (1000 samples) |
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- nvidia/OpenCodeReasoning (1000 samples) |
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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/Xiaolong-Qwen3-0.6B-Q5_K_M-GGUF --hf-file xiaolong-qwen3-0.6b-q5_k_m.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/Xiaolong-Qwen3-0.6B-Q5_K_M-GGUF --hf-file xiaolong-qwen3-0.6b-q5_k_m.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/Xiaolong-Qwen3-0.6B-Q5_K_M-GGUF --hf-file xiaolong-qwen3-0.6b-q5_k_m.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/Xiaolong-Qwen3-0.6B-Q5_K_M-GGUF --hf-file xiaolong-qwen3-0.6b-q5_k_m.gguf -c 2048 |
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``` |
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