Edit model card

OpenDolphinHermes_Llama2_7B

SynthIQ

mergekit SLERP of these two models

🧩 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
Downloads last month
264
Safetensors
Model size
6.74B params
Tensor type
BF16
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for sethuiyer/OpenDolphinHermes_Llama2_7B

Datasets used to train sethuiyer/OpenDolphinHermes_Llama2_7B

Evaluation results