metadata

license: apache-2.0
tags:
  - merge
  - mergekit
  - lazymergekit
  - abideen/MonarchCoder-7B
  - eldogbbhed/NeuralPearlBeagle
base_model:
  - abideen/MonarchCoder-7B
  - eldogbbhed/NeuralPearlBeagle

NeuralMonarchCoderPearlBeagle

NeuralMonarchCoderPearlBeagle is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [abideen/MonarchCoder-7B](https://huggingface.co./abideen/MonarchCoder-7B) * [eldogbbhed/NeuralPearlBeagle](https://huggingface.co./eldogbbhed/NeuralPearlBeagle)

🧩 Configuration

models:
  - model: abideen/MonarchCoder-7B
    parameters:
      density: 0.6
      weight: 0.5
  - model: eldogbbhed/NeuralPearlBeagle
    parameters:
      density: 0.8
      weight: 0.8
merge_method: ties
base_model: eldogbbhed/NeuralPearlBeagle
parameters:
  normalize: true
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "eldogbbhed/NeuralMonarchCoderPearlBeagle"
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"])