slices:
  - sources:
      - model: NousResearch/Genstruct-7B
        layer_range: [0, 32]
      - model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
        layer_range: [0, 32]
merge_method: slerp
base_model: NousResearch/Genstruct-7B

---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**


### 🚀 Prompt Hub
Need high-quality prompts for image models and LLMs? Explore **[findgoodprompt.com](https://findgoodprompt.com)**.
### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
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 = "artificialguybr/GenStructDolphin-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"])

Explore More

Discover more of my models, projects, and practical AI resources:

AI tools directory: JustGoodAI — a curated directory of useful AI tools.