Triangle104/Lamarck-14B-v0.7-Q8_0-GGUF

This model was converted to GGUF format from sometimesanotion/Lamarck-14B-v0.7 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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.

Lamarck is produced by a custom toolchain to automate a process of LoRAs and layer-targeting merges:

Extracted LoRA adapters from special-purpose merges
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
Separate branches for aggressive breadcrumbs and conservative DELLA merges
Highly targeted weight/density gradients for every 2-4 layers, at each stage
Finalization through SLERP+TIES merges recombining the breadcrumbs and DELLA branches to taste

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.

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.

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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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Lamarck-14B-v0.7-Q8_0-GGUF --hf-file lamarck-14b-v0.7-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Lamarck-14B-v0.7-Q8_0-GGUF --hf-file lamarck-14b-v0.7-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

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).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Lamarck-14B-v0.7-Q8_0-GGUF --hf-file lamarck-14b-v0.7-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Lamarck-14B-v0.7-Q8_0-GGUF --hf-file lamarck-14b-v0.7-q8_0.gguf -c 2048