---
tags:
- merge
- mergekit
- lazymergekit
- Weyaxi/Einstein-v6-7B
- argilla/CapybaraHermes-2.5-Mistral-7B
base_model:
- Weyaxi/Einstein-v6-7B
- argilla/CapybaraHermes-2.5-Mistral-7B
license: apache-2.0
language:
- en
- de
- es
- fr
- ja
- zh
---

# J.O.S.I.E.3-Beta12-7B-slerp

J.O.S.I.E.3-Beta12-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Weyaxi/Einstein-v6-7B](https://huggingface.co/Weyaxi/Einstein-v6-7B)
* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)

This model has been further Finetuned on my custom J.O.S.I.E.v3.11 Dataset, in the ChatML prompt Format.

[-- GGUF Quants --](https://huggingface.co/Isaak-Carter/J.O.S.I.E.3-Beta12-7B-slerp-gguf)

```text
<|im_start|>system
You are JOSIE, my private and superinteligent AI Assistant.<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
{{ .Response }}<|im_end|>
```


#### Run in ollama:


```sh
ollama run goekdenizguelmez/j.o.s.i.e.v3-beta12.1
```

Only q4-k-m for now!


## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: Weyaxi/Einstein-v6-7B
        layer_range: [0, 32]
      - model: argilla/CapybaraHermes-2.5-Mistral-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
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
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Isaak-Carter/J.O.S.I.E.3-Beta12-7B-slerp"
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"])
```

# Evaluation results:

```json
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        "mc2": 0.5816259277988214,
        "mc2_stderr": 0.01521267822060948
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}
```