Instructions to use bartowski/DPOpenHermes-7B-v2-experimental-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/DPOpenHermes-7B-v2-experimental-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/DPOpenHermes-7B-v2-experimental-exl2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/DPOpenHermes-7B-v2-experimental-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bartowski/DPOpenHermes-7B-v2-experimental-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/DPOpenHermes-7B-v2-experimental-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/DPOpenHermes-7B-v2-experimental-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/DPOpenHermes-7B-v2-experimental-exl2

SGLang

How to use bartowski/DPOpenHermes-7B-v2-experimental-exl2 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 "bartowski/DPOpenHermes-7B-v2-experimental-exl2" \
    --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": "bartowski/DPOpenHermes-7B-v2-experimental-exl2",
		"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 "bartowski/DPOpenHermes-7B-v2-experimental-exl2" \
        --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": "bartowski/DPOpenHermes-7B-v2-experimental-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bartowski/DPOpenHermes-7B-v2-experimental-exl2 with Docker Model Runner:
```
docker model run hf.co/bartowski/DPOpenHermes-7B-v2-experimental-exl2
```

Exllama v2 Quantizations of DPOpenHermes-7B-v2-experimental

Using turboderp's ExLlamaV2 experimental for quantization.

Each branch contains an individual bits per weight. This is an experimental ExLlamaV2 quantization with no measurement.json produced.

Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.

Original model: https://huggingface.co/openaccess-ai-collective/DPOpenHermes-7B-v2

Download instructions

With git:

git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/DPOpenHermes-7B-v2-experimental-exl2

With huggingface hub (credit to TheBloke for instructions):

pip3 install huggingface-hub

To download the main (only useful if you only care about measurement.json) branch to a folder called DPOpenHermes-7B-v2-experimental-exl2:

mkdir DPOpenHermes-7B-v2-experimental-exl2
huggingface-cli download bartowski/DPOpenHermes-7B-v2-experimental-exl2 --local-dir DPOpenHermes-7B-v2-experimental-exl2 --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir DPOpenHermes-7B-v2-experimental-exl2
huggingface-cli download bartowski/DPOpenHermes-7B-v2-experimental-exl2 --revision 4_0 --local-dir DPOpenHermes-7B-v2-experimental-exl2 --local-dir-use-symlinks False

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for bartowski/DPOpenHermes-7B-v2-experimental-exl2

Base model

mistralai/Mistral-7B-v0.1

Finetuned

teknium/OpenHermes-2.5-Mistral-7B

Finetuned

(83)

this model

bartowski
/

DPOpenHermes-7B-v2-experimental-exl2

Exllama v2 Quantizations of DPOpenHermes-7B-v2-experimental

Download instructions

Model tree for bartowski/DPOpenHermes-7B-v2-experimental-exl2

Datasets used to train bartowski/DPOpenHermes-7B-v2-experimental-exl2