RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf

Quantization made by Richard Erkhov.

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OpenDolphinHermesLlama27B - GGUF

• —Model creator: https://huggingface.co/sethuiyer/
• —Original model: https://huggingface.co/sethuiyer/OpenDolphinHermesLlama27B/

Original model description: --- language:

• —en license: llama2 library_name: transformers tags:
• —merge
• —mergekit
• —lazymergekit datasets:
• —teknium/openhermes
• —cognitivecomputations/dolphin base_model:
• —cognitivecomputations/dolphin-llama2-7b
• —Tensoic/Llama-2-openhermes pipeline_tag: text-generation model-index:
• —name: OpenDolphinHermesLlama27B 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: 55.03 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=sethuiyer/OpenDolphinHermesLlama2_7B 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: 78.74 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=sethuiyer/OpenDolphinHermesLlama2_7B 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: 52.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=sethuiyer/OpenDolphinHermesLlama27B 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: 46.1 source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=sethuiyer/OpenDolphinHermesLlama27B 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: 73.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=sethuiyer/OpenDolphinHermesLlama27B 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: 20.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=sethuiyer/OpenDolphinHermesLlama27B name: Open LLM Leaderboard ---

OpenDolphinHermesLlama27B

<p align="center"> <img src="https://huggingface.co/sethuiyer/OpenDolphinHermesLlama27B/resolve/main/dolphin_hermes.webp" height="256px" alt="SynthIQ"> </p>

mergekit SLERP of these two models

• —cognitivecomputations/dolphin-llama2-7b
• —Tensoic/Llama-2-openhermes

🧩 Configuration

slices:
  - sources:
      - model: cognitivecomputations/dolphin-llama2-7b
        layer_range: [0, 32]
      - model: Tensoic/Llama-2-openhermes
        layer_range: [0, 32]
merge_method: slerp
base_model: Tensoic/Llama-2-openhermes
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

Prompt Template (ChatML)

<|im_start|>system
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.
Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.
Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct.
If you don't know the answer to a question, please don't share false information.
<|im_end|>
<|im_start|>user
{ .Prompt}
<|im_end|>
<|im_start|>assistant

OpenLLM Leaderboard

Why?

I wanted a LLaMa2-7B model which is as good as base LLaMa2-13B model.

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "sethuiyer/OpenDolphinHermes_Llama2_7B"
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"])

Output:

A large language model is a type of artificial intelligence system that has been trained on a massive amount of data, often millions or even billions of words, to learn the patterns and relationships between words and phrases.
These models can then be used to generate new text, understand and translate languages, and perform various natural language processing tasks.
They have become increasingly popular in recent years due to advances in machine learning technology and their ability to achieve high levels of accuracy and performance on natural language processing tasks.
Examples of large language models include GPT-2, BERT, and T5.

Thanks

Thanks to Google Colab for the compute.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here