--- language: - en license: other library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: Nohobby/MS-Schisandra-22B-v0.3 --- # Triangle104/MS-Schisandra-22B-v0.3-Q4_K_M-GGUF This model was converted to GGUF format from [`Nohobby/MS-Schisandra-22B-v0.3`](https://huggingface.co/Nohobby/MS-Schisandra-22B-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Nohobby/MS-Schisandra-22B-v0.3) for more details on the model. Merge Details - Merging steps Karasik-v0.3 models: - model: Mistral-Small-22B-ArliAI-RPMax-v1.1 parameters: weight: [0.2, 0.3, 0.2, 0.3, 0.2] density: [0.45, 0.55, 0.45, 0.55, 0.45] - model: Mistral-Small-NovusKyver parameters: weight: [0.01768, -0.01675, 0.01285, -0.01696, 0.01421] density: [0.6, 0.4, 0.5, 0.4, 0.6] - model: MiS-Firefly-v0.2-22B parameters: weight: [0.208, 0.139, 0.139, 0.139, 0.208] density: [0.7] - model: magnum-v4-22b parameters: weight: [0.33] density: [0.45, 0.55, 0.45, 0.55, 0.45] merge_method: della_linear base_model: Mistral-Small-22B-ArliAI-RPMax-v1.1 parameters: epsilon: 0.05 lambda: 1.05 int8_mask: true rescale: true normalize: false dtype: bfloat16 tokenizer_source: base SchisandraVA3 (Config taken from here) merge_method: della_linear dtype: bfloat16 parameters: normalize: true int8_mask: true tokenizer_source: base base_model: Cydonia-22B-v1.3 models: - model: Karasik03 parameters: density: 0.55 weight: 1 - model: Pantheon-RP-Pure-1.6.2-22b-Small parameters: density: 0.55 weight: 1 - model: ChatWaifu_v2.0_22B parameters: density: 0.55 weight: 1 - model: MS-Meadowlark-Alt-22B parameters: density: 0.55 weight: 1 - model: SorcererLM-22B parameters: density: 0.55 weight: 1 Schisandra-v0.3 dtype: bfloat16 tokenizer_source: base merge_method: della_linear parameters: density: 0.5 base_model: SchisandraVA3 models: - model: unsloth/Mistral-Small-Instruct-2409 parameters: weight: - filter: v_proj value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0] - filter: o_proj value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1] - filter: up_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - filter: gate_proj value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0] - filter: down_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - value: 0 - model: SchisandraVA3 parameters: weight: - filter: v_proj value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1] - filter: o_proj value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0] - filter: up_proj value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - filter: gate_proj value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1] - filter: down_proj value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] - value: 1 --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/MS-Schisandra-22B-v0.3-Q4_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/MS-Schisandra-22B-v0.3-Q4_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q4_k_m.gguf -c 2048 ``` 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. 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/MS-Schisandra-22B-v0.3-Q4_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/MS-Schisandra-22B-v0.3-Q4_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q4_k_m.gguf -c 2048 ```