ArchBeagle-7B
ArchBeagle-7B is a merge of the following models using LazyMergekit:
- shadowml/WestBeagle-7B
- mlabonne/NeuralBeagle14-7B
- shadowml/BeagSake-7B
- mlabonne/NeuralOmniBeagle-7B-v2
- mlabonne/NeuralOmniBeagle-7B
- mlabonne/OmniBeagle-7B
🧩 Configuration
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: shadowml/WestBeagle-7B
parameters:
density: 0.65
weight: 0.25
- model: mlabonne/NeuralBeagle14-7B
parameters:
density: 0.6
weight: 0.15
- model: shadowml/BeagSake-7B
parameters:
density: 0.55
weight: 0.1
- model: mlabonne/NeuralOmniBeagle-7B-v2
parameters:
density: 0.65
weight: 0.25
- model: mlabonne/NeuralOmniBeagle-7B
parameters:
density: 0.6
weight: 0.15
- model: mlabonne/OmniBeagle-7B
parameters:
density: 0.55
weight: 0.1
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/ArchBeagle-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"])