OpenDolphinHermes_Llama2_7B/README.md

language, license, library_name, tags, datasets, base_model, pipeline_tag, model-index

language	license	library_name	tags	datasets	base_model	pipeline_tag	model-index
en	llama2	transformers	merge mergekit lazymergekit	teknium/openhermes cognitivecomputations/dolphin	cognitivecomputations/dolphin-llama2-7b Tensoic/Llama-2-openhermes	text-generation	name results OpenDolphinHermes_Llama2_7B task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 55.03 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 78.74 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 52.25 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 46.1 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 73.16 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 20.17 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B Open LLM Leaderboard

OpenDolphinHermes_Llama2_7B

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

T	Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
0	meta-llama/llama-2-13b-hf	55.69	59.39	82.13	55.77	37.38	76.64	22.82
1	sethuiyer/OpenDolphinHermes_Llama2_7B	54.24	55.03	78.74	52.25	46.1	73.16	20.17
2	togethercomputer/Llama-2-7B-32K-Instruct	50.02	51.11	78.51	46.11	44.86	73.88	5.69
3	togethercomputer/LLaMa-2-7B-32K	47.07	47.53	76.14	43.33	39.23	71.9	4.32

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

Metric	Value
Avg.	54.24
AI2 Reasoning Challenge (25-Shot)	55.03
HellaSwag (10-Shot)	78.74
MMLU (5-Shot)	52.25
TruthfulQA (0-shot)	46.10
Winogrande (5-shot)	73.16
GSM8k (5-shot)	20.17