Triangle104/MS-Schisandra-22B-v0.3-Q5_K_M-GGUF

This model was converted to GGUF format from Nohobby/MS-Schisandra-22B-v0.3 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card 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)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/MS-Schisandra-22B-v0.3-Q5_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/MS-Schisandra-22B-v0.3-Q5_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q5_k_m.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/MS-Schisandra-22B-v0.3-Q5_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/MS-Schisandra-22B-v0.3-Q5_K_M-GGUF --hf-file ms-schisandra-22b-v0.3-q5_k_m.gguf -c 2048