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RichardErkhov/
leveldevai_-_MarcBeagle-7B-gguf

Name: leveldevai_-_MarcBeagle-7B-gguf
Availability: InStock

endpoints_compatible

gguf

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MarcBeagle-7B - GGUF

Model creator: https://huggingface.co/leveldevai/
Original model: https://huggingface.co/leveldevai/MarcBeagle-7B/

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

Original model description:

license: apache-2.0 tags:

merge
mergekit
lazymergekit
flemmingmiguel/MarcMistral-7B
leveldevai/TurdusBeagle-7B

MarcBeagle-7B

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

🧩 Configuration

yaml

slices:
    - sources:
      - model: flemmingmiguel/MarcMistral-7B
        layer_range: [0, 32]
      - model: leveldevai/TurdusBeagle-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: leveldevai/TurdusBeagle-7B
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: 0.45 # fallback for rest of tensors
dtype: float16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "leveldevai/MarcBeagle-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/leveldevai_-_MarcBeagle-7B-gguf

Original model description:

MarcBeagle-7B

🧩 Configuration

💻 Usage

RichardErkhov/
leveldevai_-_MarcBeagle-7B-gguf