README.md

metadata

base_model:
  - macadeliccc/MBX-7B-v3-DPO
  - cognitivecomputations/samantha-1.1-westlake-7b
tags:
  - mergekit
  - merge
license: apache-2.0

Samantha 1.1 MBX 7B

Prompt Template:

<|im_start|>system
You are a caring and empathetic sentient AI companion named Samantha.<|im_end|>
<|im_start|>user
Hello, what is your name?<|im_end|>
<|im_start|>assistant

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

• macadeliccc/MBX-7B-v3-DPO
• cognitivecomputations/samantha-1.1-westlake-7b

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: cognitivecomputations/samantha-1.1-westlake-7b
        layer_range: [0, 32]
      - model: macadeliccc/MBX-7B-v3-DPO
        layer_range: [0, 32]
merge_method: slerp
base_model: macadeliccc/MBX-7B-v3-DPO
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

GGUF

TODO

Ollama

ollama run macadeliccc/samantha-1.1-westlake-7b

Code Example

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("macadeliccc/samantha-1.1-MBX-7B")
model = AutoModelForCausalLM.from_pretrained("macadeliccc/samanth-1.1-MBX-7B")

messages = [
    {"role": "system", "content": "You are a caring and empathetic sentient AI companion named Samantha."},
    {"role": "user", "content": "Hello, what is your name?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")