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RichardErkhov/
sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf

Name: sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf
Availability: InStock

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OpenDolphinHermes_Llama2_7B - GGUF

Model creator: https://huggingface.co/sethuiyer/
Original model: https://huggingface.co/sethuiyer/OpenDolphinHermes_Llama2_7B/

Name	Quant method	Size
OpenDolphinHermes_Llama2_7B.Q2_K.gguf	Q2_K	2.36GB
OpenDolphinHermes_Llama2_7B.IQ3_XS.gguf	IQ3_XS	2.6GB
OpenDolphinHermes_Llama2_7B.IQ3_S.gguf	IQ3_S	2.75GB
OpenDolphinHermes_Llama2_7B.Q3_K_S.gguf	Q3_K_S	2.75GB
OpenDolphinHermes_Llama2_7B.IQ3_M.gguf	IQ3_M	2.9GB
OpenDolphinHermes_Llama2_7B.Q3_K.gguf	Q3_K	3.07GB
OpenDolphinHermes_Llama2_7B.Q3_K_M.gguf	Q3_K_M	3.07GB
OpenDolphinHermes_Llama2_7B.Q3_K_L.gguf	Q3_K_L	3.35GB
OpenDolphinHermes_Llama2_7B.IQ4_XS.gguf	IQ4_XS	3.4GB
OpenDolphinHermes_Llama2_7B.Q4_0.gguf	Q4_0	3.56GB
OpenDolphinHermes_Llama2_7B.IQ4_NL.gguf	IQ4_NL	3.58GB
OpenDolphinHermes_Llama2_7B.Q4_K_S.gguf	Q4_K_S	3.59GB
OpenDolphinHermes_Llama2_7B.Q4_K.gguf	Q4_K	3.8GB
OpenDolphinHermes_Llama2_7B.Q4_K_M.gguf	Q4_K_M	3.8GB
OpenDolphinHermes_Llama2_7B.Q4_1.gguf	Q4_1	3.95GB
OpenDolphinHermes_Llama2_7B.Q5_0.gguf	Q5_0	4.33GB
OpenDolphinHermes_Llama2_7B.Q5_K_S.gguf	Q5_K_S	4.33GB
OpenDolphinHermes_Llama2_7B.Q5_K.gguf	Q5_K	4.45GB
OpenDolphinHermes_Llama2_7B.Q5_K_M.gguf	Q5_K_M	4.45GB
OpenDolphinHermes_Llama2_7B.Q5_1.gguf	Q5_1	4.72GB
OpenDolphinHermes_Llama2_7B.Q6_K.gguf	Q6_K	5.15GB
OpenDolphinHermes_Llama2_7B.Q8_0.gguf	Q8_0	6.67GB

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: OpenDolphinHermes_Llama2_7B results:
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm value: 55.03 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm value: 78.74 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_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: num_few_shot: 5 metrics:
  - type: acc value: 52.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 46.1 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc value: 73.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 20.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B name: Open LLM Leaderboard

OpenDolphinHermes_Llama2_7B

mergekit SLERP of these two models

🧩 Configuration

yaml

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)

text

<|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

python

!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:

text

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

RichardErkhov/sethuiyer_-_OpenDolphinHermes_Llama2_7B-gguf