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---
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](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO)
* [cognitivecomputations/samantha-1.1-westlake-7b](https://huggingface.co/cognitivecomputations/samantha-1.1-westlake-7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
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
```bash
ollama run macadeliccc/samantha-1.1-westlake-7b
```
## Code Example
```python
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")
```
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