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RichardErkhov/
allknowingroger_-_MultiMash6-12B-slerp-gguf

Name: allknowingroger_-_MultiMash6-12B-slerp-gguf
Availability: InStock

endpoints_compatible

gguf

template

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Quantization made by Richard Erkhov.

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MultiMash6-12B-slerp - GGUF

Model creator: https://huggingface.co/allknowingroger/
Original model: https://huggingface.co/allknowingroger/MultiMash6-12B-slerp/

Name	Quant method	Size
MultiMash6-12B-slerp.Q2_K.gguf	Q2_K	4.43GB
MultiMash6-12B-slerp.IQ3_XS.gguf	IQ3_XS	4.95GB
MultiMash6-12B-slerp.IQ3_S.gguf	IQ3_S	5.22GB
MultiMash6-12B-slerp.Q3_K_S.gguf	Q3_K_S	5.2GB
MultiMash6-12B-slerp.IQ3_M.gguf	IQ3_M	5.35GB
MultiMash6-12B-slerp.Q3_K.gguf	Q3_K	5.78GB
MultiMash6-12B-slerp.Q3_K_M.gguf	Q3_K_M	5.78GB
MultiMash6-12B-slerp.Q3_K_L.gguf	Q3_K_L	6.27GB
MultiMash6-12B-slerp.IQ4_XS.gguf	IQ4_XS	6.5GB
MultiMash6-12B-slerp.Q4_0.gguf	Q4_0	6.78GB
MultiMash6-12B-slerp.IQ4_NL.gguf	IQ4_NL	6.85GB
MultiMash6-12B-slerp.Q4_K_S.gguf	Q4_K_S	6.84GB
MultiMash6-12B-slerp.Q4_K.gguf	Q4_K	7.25GB
MultiMash6-12B-slerp.Q4_K_M.gguf	Q4_K_M	7.25GB
MultiMash6-12B-slerp.Q4_1.gguf	Q4_1	7.52GB
MultiMash6-12B-slerp.Q5_0.gguf	Q5_0	8.26GB
MultiMash6-12B-slerp.Q5_K_S.gguf	Q5_K_S	8.26GB
MultiMash6-12B-slerp.Q5_K.gguf	Q5_K	8.51GB
MultiMash6-12B-slerp.Q5_K_M.gguf	Q5_K_M	8.51GB
MultiMash6-12B-slerp.Q5_1.gguf	Q5_1	9.01GB
MultiMash6-12B-slerp.Q6_K.gguf	Q6_K	9.84GB
MultiMash6-12B-slerp.Q8_0.gguf	Q8_0	12.75GB

Original model description:

tags:

merge
mergekit
lazymergekit
allknowingroger/Multimerge-Neurallaymons-12B-MoE
RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2 base_model:
allknowingroger/Multimerge-Neurallaymons-12B-MoE
RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2 license: apache-2.0

MultiMash6-12B-slerp

MultiMash6-12B-slerp is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml

slices:
  - sources:
      - model: allknowingroger/Multimerge-Neurallaymons-12B-MoE
        layer_range: [0, 32]
      - model: RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2
        layer_range: [0, 32]
merge_method: slerp
base_model: allknowingroger/Multimerge-Neurallaymons-12B-MoE
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "allknowingroger/MultiMash6-12B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

RichardErkhov/allknowingroger_-_MultiMash6-12B-slerp-gguf

Original model description:

MultiMash6-12B-slerp

🧩 Configuration

💻 Usage

RichardErkhov/
allknowingroger_-_MultiMash6-12B-slerp-gguf