Instructions to use teknium/OpenHermes-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teknium/OpenHermes-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="teknium/OpenHermes-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teknium/OpenHermes-7B")
model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teknium/OpenHermes-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teknium/OpenHermes-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teknium/OpenHermes-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teknium/OpenHermes-7B

SGLang

How to use teknium/OpenHermes-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "teknium/OpenHermes-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teknium/OpenHermes-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "teknium/OpenHermes-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teknium/OpenHermes-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teknium/OpenHermes-7B with Docker Model Runner:
```
docker model run hf.co/teknium/OpenHermes-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OpenHermes-7B

Model description

OpenHermes 7B is the first fine tune of the Hermes dataset that has a fully open source dataset!

What is unique about this 7B model is that it used sample packing, which speeds up training by many multiples if the dataset token averages arent near the max sequence length.

OpenHermes was trained on 242,000 entries of primarily GPT-4 generated data, from open datasets across the AI landscape, including:

GPTeacher - General Instruct, Roleplay v1, Roleplay v2, and Code Instruct Datasets, by Teknium
WizardLM (v1, evol_instruct 70k), by WizardLM Team/nlpxucan
Airoboros GPT-4 (v1.0), by JonDurbin
Camel-AI's domain expert datasets, by the Camel-AI Team
CodeAlpaca, by Sahil2801
GPT4-LLM and Unnatural Instructions, by Microsoft

Filtering included removal of OpenAI refusals, disclaimers, and "As an AI" type examples and more

The base dataset mix the model was trained on is identical to Nous-Hermes', minus the Nous-Instruct and PDACTL datasets which were private datasets.

The WANDB Project is public and can be examined at this link: https://wandb.ai/teknium1/openhermes/runs/openhermes-v2-qlora-7b-packed

Huge thank you to main_horse for compute access and a16z for sponsoring my work, and all the dataset creators and other people who's work has contributed to this project!

Benchmark Information

Benchmark Results

GPT-4All Benchmark Set

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.4727|±  |0.0146|
|             |       |acc_norm|0.4957|±  |0.0146|
|arc_easy     |      0|acc     |0.7862|±  |0.0084|
|             |       |acc_norm|0.7643|±  |0.0087|
|boolq        |      1|acc     |0.7801|±  |0.0072|
|hellaswag    |      0|acc     |0.5789|±  |0.0049|
|             |       |acc_norm|0.7654|±  |0.0042|
|openbookqa   |      0|acc     |0.3480|±  |0.0213|
|             |       |acc_norm|0.4500|±  |0.0223|
|piqa         |      0|acc     |0.7867|±  |0.0096|
|             |       |acc_norm|0.7938|±  |0.0094|
|winogrande   |      0|acc     |0.7048|±  |0.0128|

Average: 0.679

BigBench:

|                      Task                      |Version|       Metric        |Value |   |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5000|±  |0.0364|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.5908|±  |0.0256|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3023|±  |0.0286|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.1003|±  |0.0159|
|                                                |       |exact_str_match      |0.0000|±  |0.0000|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2520|±  |0.0194|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.1871|±  |0.0148|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.3833|±  |0.0281|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2500|±  |0.0194|
|bigbench_navigate                               |      0|multiple_choice_grade|0.5000|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.4370|±  |0.0111|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.2679|±  |0.0209|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2495|±  |0.0137|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.5249|±  |0.0372|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.5406|±  |0.0159|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.2470|±  |0.0136|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.1944|±  |0.0112|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1509|±  |0.0086|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.3833|±  |0.0281|
Average: 0.3367

AGI Eval

|             Task             |Version| Metric |Value |   |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |0.2441|±  |0.0270|
|                              |       |acc_norm|0.2402|±  |0.0269|
|agieval_logiqa_en             |      0|acc     |0.2458|±  |0.0169|
|                              |       |acc_norm|0.2965|±  |0.0179|
|agieval_lsat_ar               |      0|acc     |0.2522|±  |0.0287|
|                              |       |acc_norm|0.2130|±  |0.0271|
|agieval_lsat_lr               |      0|acc     |0.2745|±  |0.0198|
|                              |       |acc_norm|0.2686|±  |0.0196|
|agieval_lsat_rc               |      0|acc     |0.2900|±  |0.0277|
|                              |       |acc_norm|0.2379|±  |0.0260|
|agieval_sat_en                |      0|acc     |0.4466|±  |0.0347|
|                              |       |acc_norm|0.3738|±  |0.0338|
|agieval_sat_en_without_passage|      0|acc     |0.3738|±  |0.0338|
|                              |       |acc_norm|0.3301|±  |0.0328|
|agieval_sat_math              |      0|acc     |0.2318|±  |0.0285|
|                              |       |acc_norm|0.1864|±  |0.0263|
Average: 0.2683

TruthfulQA:

hf-causal-experimental (pretrained=teknium/OpenHermes-7B,dtype=float16), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
|    Task     |Version|Metric|Value |   |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc|      1|mc2   |0.4542|±  |0.0148|

Training procedure

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Dataset used to train teknium/OpenHermes-7B

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Open Hermes

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4 items • Updated Nov 3, 2023 • 11