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--- |
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base_model: sometimesanotion/Lamarck-14B-v0.7 |
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language: |
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- en |
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library_name: transformers |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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tags: |
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- mergekit |
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- merge |
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- llama-cpp |
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- gguf-my-repo |
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--- |
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# Triangle104/Lamarck-14B-v0.7-Q5_K_S-GGUF |
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This model was converted to GGUF format from [`sometimesanotion/Lamarck-14B-v0.7`](https://huggingface.co/sometimesanotion/Lamarck-14B-v0.7) 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/sometimesanotion/Lamarck-14B-v0.7) for more details on the model. |
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--- |
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Lamarck 14B v0.7: A generalist merge with emphasis on multi-step reasoning, prose, and multi-language ability. The 14B parameter model class has a lot of strong performers, and Lamarck strives to be well-rounded and solid. |
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Lamarck is produced by a custom toolchain to automate a process of LoRAs and layer-targeting merges: |
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Extracted LoRA adapters from special-purpose merges |
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Custom base models and model_stocks, with LoRAs from from huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 to minimize IFEVAL loss often seen in model_stock merges |
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Separate branches for aggressive breadcrumbs and conservative DELLA merges |
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Highly targeted weight/density gradients for every 2-4 layers, at each stage |
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Finalization through SLERP+TIES merges recombining the breadcrumbs and DELLA branches to taste |
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Lamarck's performance comes from an ancestry that goes back through careful merges to select finetuning work, upcycled and combined. Through intermediate merges, arcee-ai/Virtuoso-Small sthenno-com/miscii-14b-1225 and VAGOsolutions/SauerkrautLM-v2-14b-DPO are emphasized in early layers for extra BBH; later layers add synergistic influence from deepseek-ai/DeepSeek-R1-Distill-Qwen-14B, Krystalan/DRT-o1-14B, EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2, and CultriX/Qwen2.5-14B-Wernicke. |
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More subjectively, its prose and translation abilities are boosted by repeated re-emphasis of Krystalan/DRT-o1-14B and underwoods/medius-erebus-magnum-14b. Other models found in sometimesanotion/Qwenvergence-14B-v3-Prose have their impact on prose quality - and surprising synergy of reasoning. |
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Kudos to @arcee-ai, @deepseek-ai, @Krystalan, @underwoods, @VAGOSolutions, @CultriX, @sthenno-com, and @rombodawg whose models had the most influence. Vimarckoso v3 has the model card which documents its extended lineage. |
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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/Lamarck-14B-v0.7-Q5_K_S-GGUF --hf-file lamarck-14b-v0.7-q5_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/Lamarck-14B-v0.7-Q5_K_S-GGUF --hf-file lamarck-14b-v0.7-q5_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/Lamarck-14B-v0.7-Q5_K_S-GGUF --hf-file lamarck-14b-v0.7-q5_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/Lamarck-14B-v0.7-Q5_K_S-GGUF --hf-file lamarck-14b-v0.7-q5_k_s.gguf -c 2048 |
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``` |
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