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RichardErkhov/
paulml_-_OmniBeagleSquaredMBX-v3-7B-gguf

Name: paulml_-_OmniBeagleSquaredMBX-v3-7B-gguf
Availability: InStock

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gguf

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Quantization made by Richard Erkhov.

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OmniBeagleSquaredMBX-v3-7B - GGUF

Model creator: https://huggingface.co/paulml/
Original model: https://huggingface.co/paulml/OmniBeagleSquaredMBX-v3-7B/

Name	Quant method	Size
OmniBeagleSquaredMBX-v3-7B.Q2_K.gguf	Q2_K	2.53GB
OmniBeagleSquaredMBX-v3-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
OmniBeagleSquaredMBX-v3-7B.IQ3_S.gguf	IQ3_S	2.96GB
OmniBeagleSquaredMBX-v3-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
OmniBeagleSquaredMBX-v3-7B.IQ3_M.gguf	IQ3_M	3.06GB
OmniBeagleSquaredMBX-v3-7B.Q3_K.gguf	Q3_K	3.28GB
OmniBeagleSquaredMBX-v3-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
OmniBeagleSquaredMBX-v3-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
OmniBeagleSquaredMBX-v3-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
OmniBeagleSquaredMBX-v3-7B.Q4_0.gguf	Q4_0	3.83GB
OmniBeagleSquaredMBX-v3-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
OmniBeagleSquaredMBX-v3-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
OmniBeagleSquaredMBX-v3-7B.Q4_K.gguf	Q4_K	4.07GB
OmniBeagleSquaredMBX-v3-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
OmniBeagleSquaredMBX-v3-7B.Q4_1.gguf	Q4_1	4.24GB
OmniBeagleSquaredMBX-v3-7B.Q5_0.gguf	Q5_0	4.65GB
OmniBeagleSquaredMBX-v3-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
OmniBeagleSquaredMBX-v3-7B.Q5_K.gguf	Q5_K	4.78GB
OmniBeagleSquaredMBX-v3-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
OmniBeagleSquaredMBX-v3-7B.Q5_1.gguf	Q5_1	5.07GB
OmniBeagleSquaredMBX-v3-7B.Q6_K.gguf	Q6_K	5.53GB
OmniBeagleSquaredMBX-v3-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

tags:

merge
mergekit
lazymergekit
paulml/OmniBeagleMBX-v3-7B
flemmingmiguel/MBX-7B-v3 base_model:
paulml/OmniBeagleMBX-v3-7B
flemmingmiguel/MBX-7B-v3 license: cc-by-nc-4.0

As of February 12th, 2024, this model ranks number one in the Arc Challenge for 7B models.

OmniBeagleSquaredMBX-v3-7B

OmniBeagleSquaredMBX-v3-7B is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml

slices:
  - sources:
      - model: paulml/OmniBeagleMBX-v3-7B
        layer_range: [0, 32]
      - model: flemmingmiguel/MBX-7B-v3
        layer_range: [0, 32]
merge_method: slerp
base_model: flemmingmiguel/MBX-7B-v3
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 = "paulml/OmniBeagleSquaredMBX-v3-7B"
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/paulml_-_OmniBeagleSquaredMBX-v3-7B-gguf