README.md

---
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

![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/bq3IRI6barhEzd1E9LbEe.webp)

**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")
```