mlabonne/NeuralMonarch-7B
LoRA-style vs mlabonne/Monarch-7B. Looks like a merged LoRA (or a fine-tune with frozen norms): only the weight matrices of down_proj, gate_proj, k_proj, o_proj, q_proj, up_proj, v_proj changed, in 32 of 32 layers; norms, biases and embeddings are untouched.
Tensors changed: Embeddings 0/1, Norms 0/65, MLP 96/96, Attention 128/128, lm_head 0/1
8 derivatives checked
- mlabonne/AlphaMonarch-7B: LoRA-style
- mradermacher/NeuralMonarch-7B-i1-GGUF: Quantization
- theprint/Boptruth-NeuralMonarch-7B: Full fine-tune
- mradermacher/NeuralMonarch-7B-GGUF: Same weights, converted
- tensorblock/NeuralMonarch-7B-GGUF: Quantization
- abideen/AlphaMonarch-dora: LoRA-style
- abideen/AlphaMonarch-laser: LoRA-style
- abideen/AlphaMonarch-daser: LoRA-style