Instructions to use teknium/OpenHermes-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teknium/OpenHermes-13B with Transformers:

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

pipe = pipeline("text-generation", model="teknium/OpenHermes-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teknium/OpenHermes-13B")
model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-13B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teknium/OpenHermes-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teknium/OpenHermes-13B"
# 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-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teknium/OpenHermes-13B

SGLang

How to use teknium/OpenHermes-13B 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-13B" \
    --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-13B",
		"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-13B" \
        --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-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teknium/OpenHermes-13B with Docker Model Runner:
```
docker model run hf.co/teknium/OpenHermes-13B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OpenHermes-13B

Model description

OpenHermes 13B is the first fine tune of the Hermes dataset that has a fully open source dataset!

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-fullft-13b

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!

Example Outputs

Benchmark Information

Benchmark Results

GPT-4All Benchmark Set

|    Task     |Version| Metric |Value |   |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge|      0|acc     |0.5009|±  |0.0146|
|             |       |acc_norm|0.5247|±  |0.0146|
|arc_easy     |      0|acc     |0.8127|±  |0.0080|
|             |       |acc_norm|0.7854|±  |0.0084|
|boolq        |      1|acc     |0.8153|±  |0.0068|
|hellaswag    |      0|acc     |0.6126|±  |0.0049|
|             |       |acc_norm|0.7995|±  |0.0040|
|openbookqa   |      0|acc     |0.3660|±  |0.0216|
|             |       |acc_norm|0.4600|±  |0.0223|
|piqa         |      0|acc     |0.7922|±  |0.0095|
|             |       |acc_norm|0.8112|±  |0.0091|
|winogrande   |      0|acc     |0.7293|±  |0.0125|
Average: 0.7036

AGI-Eval

|             Task             |Version| Metric |Value |   |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |0.2008|±  |0.0252|
|                              |       |acc_norm|0.2126|±  |0.0257|
|agieval_logiqa_en             |      0|acc     |0.3410|±  |0.0186|
|                              |       |acc_norm|0.3564|±  |0.0188|
|agieval_lsat_ar               |      0|acc     |0.2261|±  |0.0276|
|                              |       |acc_norm|0.2174|±  |0.0273|
|agieval_lsat_lr               |      0|acc     |0.3725|±  |0.0214|
|                              |       |acc_norm|0.3373|±  |0.0210|
|agieval_lsat_rc               |      0|acc     |0.4684|±  |0.0305|
|                              |       |acc_norm|0.4572|±  |0.0304|
|agieval_sat_en                |      0|acc     |0.6553|±  |0.0332|
|                              |       |acc_norm|0.5971|±  |0.0343|
|agieval_sat_en_without_passage|      0|acc     |0.4515|±  |0.0348|
|                              |       |acc_norm|0.4029|±  |0.0343|
|agieval_sat_math              |      0|acc     |0.3273|±  |0.0317|
|                              |       |acc_norm|0.2636|±  |0.0298|
Average: 0.3556

BigBench Reasoning Test

|                      Task                      |Version|       Metric        |Value |   |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5368|±  |0.0363|
|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7127|±  |0.0236|
|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|0.2720|±  |0.0199|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.1986|±  |0.0151|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4500|±  |0.0288|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2880|±  |0.0203|
|bigbench_navigate                               |      0|multiple_choice_grade|0.5000|±  |0.0158|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.5390|±  |0.0111|
|bigbench_ruin_names                             |      0|multiple_choice_grade|0.3906|±  |0.0231|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.1844|±  |0.0123|
|bigbench_snarks                                 |      0|multiple_choice_grade|0.5249|±  |0.0372|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.5335|±  |0.0159|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.2980|±  |0.0145|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2048|±  |0.0114|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1297|±  |0.0080|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4500|±  |0.0288|
Average: 36.75

This is a slight improvement on GPT4ALL Suite and BigBench Suite, with a degredation in AGIEval compared to the original hermes.

Average Score Comparison between Nous-Hermes Llama-2 and OpenHermes Llama-2:

|             Bench            | Nous-Hermes | OpenHermes | Change |
|------------------------------|------------:|------------|--------|
|GPT4All                       |        70.00|       70.36|   +0.36|
|------------------------------------------------------------------|
|BigBench                      |        36.57|       36.75|   +0.18|
|------------------------------------------------------------------|
|AGI Eval                      |        37.20|       35.56|   -1.64|