RichardErkhov/mayacinka_-_ramonda-7b-dpo-ties-gguf

Quantization made by Richard Erkhov.

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ramonda-7b-dpo-ties - GGUF

• —Model creator: https://huggingface.co/mayacinka/
• —Original model: https://huggingface.co/mayacinka/ramonda-7b-dpo-ties/

Original model description: --- license: apache-2.0 tags:

• —merge
• —mergekit
• —lazymergekit
• —paulml/OGNO-7B
• —bardsai/jaskier-7b-dpo-v4.3 base_model:
• —paulml/OGNO-7B
• —bardsai/jaskier-7b-dpo-v4.3 model-index:
• —name: Buttercup-7b-dpo-ties results:
• —task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2arc config: ARC-Challenge split: test args: numfew_shot: 25 metrics:
• —type: accnorm value: 72.7 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=mayacinka/Buttercup-7b-dpo-ties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: numfewshot: 10 metrics:
• —type: accnorm value: 89.09 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=mayacinka/Buttercup-7b-dpo-ties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: numfewshot: 5 metrics:
• —type: acc value: 64.5 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=mayacinka/Buttercup-7b-dpo-ties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthfulqa config: multiplechoice split: validation args: numfewshot: 0 metrics:
• —type: mc2 value: 77.17 source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=mayacinka/Buttercup-7b-dpo-ties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winograndexl split: validation args: numfew_shot: 5 metrics:
• —type: acc value: 84.77 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=mayacinka/Buttercup-7b-dpo-ties name: Open LLM Leaderboard
• —task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: numfewshot: 5 metrics:
• —type: acc value: 68.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=mayacinka/Buttercup-7b-dpo-ties name: Open LLM Leaderboard --- [image]

ramonda-7b-dpo-ties

ramonda-7b-dpo-ties is a merge of the following models using LazyMergekit:

• —paulml/OGNO-7B
• —bardsai/jaskier-7b-dpo-v4.3

Benchmark

Open LLM Leaderboard | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |------------------------|--------:|-----:|----------:|-----:|-----------:|-----------:|------:| | mayacinka/ramonda-7b-dpo-ties | 76.19 | 72.7 | 89.69| 64.5 | 77.17 | 84.77 | 68.92|

LLM AutoEval | Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average | |----------------------|---------|---------|------------|----------|---------| | ramonda-7b-dpo-ties | 44.67 | 77.16 | 77.6 | 49.06 | 62.12 |

🧩 Configuration

models:
  - model: bardsai/jaskier-7b-dpo-v5.6
    # no parameters necessary for base model
  - model: paulml/OGNO-7B
    parameters:
      density: 0.9
      weight: 0.5
  - model: bardsai/jaskier-7b-dpo-v4.3
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: bardsai/jaskier-7b-dpo-v5.6
parameters:
  normalize: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/ramonda-7b-dpo-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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here