Edit model card

Mem-Beagle-7b-slerp-v3

Mem-Beagle-7b-slerp-v3 is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: starsnatched/MemGPT
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: 222gate/Ingot-7b-slerp-7-forged-mirror
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: starsnatched/MemGPT
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: liminerity/Mem-Beagle-7b-slerp-v2
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "liminerity/Mem-Beagle-7b-slerp-v3"
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"])
Downloads last month
12
Safetensors
Model size
7.24B params
Tensor type
BF16
Β·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for limin-arc/Mem-Beagle-7b-slerp-v3