transformers safetensors mistral text-generation merge mergekit lazymergekit Kukedlc/Fasciculus-Arcuatus-7B-slerp Kukedlc/NeuralExperiment-7b-MagicCoder-v7 Kukedlc/Neural4gsm8k base_model:Kukedlc/Fasciculus-Arcuatus-7B-slerp base_model:Kukedlc/NeuralExperiment-7b-MagicCoder-v7 base_model:Kukedlc/Neural4gsm8k autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- merge
- mergekit
- lazymergekit
- Kukedlc/Fasciculus-Arcuatus-7B-slerp
- Kukedlc/NeuralExperiment-7b-MagicCoder-v7
- Kukedlc/Neural4gsm8k
base_model:
- Kukedlc/Fasciculus-Arcuatus-7B-slerp
- Kukedlc/NeuralExperiment-7b-MagicCoder-v7
- Kukedlc/Neural4gsm8k
---

# Neural-Logical-Abstract-7B-slerp

Neural-Logical-Abstract-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/Fasciculus-Arcuatus-7B-slerp](https://huggingface.co/Kukedlc/Fasciculus-Arcuatus-7B-slerp)
* [Kukedlc/NeuralExperiment-7b-MagicCoder-v7](https://huggingface.co/Kukedlc/NeuralExperiment-7b-MagicCoder-v7)
* [Kukedlc/Neural4gsm8k](https://huggingface.co/Kukedlc/Neural4gsm8k)

## 🧩 Configuration

```yaml
models:
  - model: Kukedlc/Fasciculus-Arcuatus-7B-slerp
    # no parameters necessary for base model
  - model: Kukedlc/Fasciculus-Arcuatus-7B-slerp
    parameters:
      density: 0.65
      weight: 0.3
  - model: Kukedlc/NeuralExperiment-7b-MagicCoder-v7
    parameters:
      density: 0.6
      weight: 0.5
  - model: Kukedlc/Neural4gsm8k
    parameters:
      density: 0.6
      weight: 0.2
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/Neural-Logical-Abstract-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"])
```