Gille_-_StrangeMerges_19-7B-dare_ties-8bits

Quantization made by Richard Erkhov.

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StrangeMerges_19-7B-dare_ties - bnb 8bits

• Model creator: https://huggingface.co/Gille/
• Original model: https://huggingface.co/Gille/StrangeMerges_19-7B-dare_ties/

Original model description:

tags:

• merge
• mergekit
• lazymergekit
• Gille/StrangeMerges_18-7B-dare_ties
• Gille/StrangeMerges_8-7B-slerp base_model:
• Gille/StrangeMerges_18-7B-dare_ties
• Gille/StrangeMerges_8-7B-slerp

---

StrangeMerges_19-7B-dare_ties

StrangeMerges_19-7B-dare_ties is a merge of the following models using LazyMergekit:

• Gille/StrangeMerges_18-7B-dare_ties
• Gille/StrangeMerges_8-7B-slerp

🧩 Configuration

models:
  - model: Gille/StrangeMerges_18-7B-dare_ties
    # no parameters necessary for base model
  - model: Gille/StrangeMerges_18-7B-dare_ties
    parameters:
      density: 0.5
      weight: 0.4
  - model: Gille/StrangeMerges_8-7B-slerp
    parameters:
      density: 0.5
      weight: 0.6
merge_method: dare_ties
base_model: Gille/StrangeMerges_18-7B-dare_ties
parameters:
  normalize: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Gille/StrangeMerges_19-7B-dare_ties"
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"])