Instructions to use thobuiq/openhermes-mistral-dpo-gptq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thobuiq/openhermes-mistral-dpo-gptq with Transformers:

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

pipe = pipeline("text-generation", model="thobuiq/openhermes-mistral-dpo-gptq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("thobuiq/openhermes-mistral-dpo-gptq")
model = AutoModelForMultimodalLM.from_pretrained("thobuiq/openhermes-mistral-dpo-gptq")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thobuiq/openhermes-mistral-dpo-gptq with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/thobuiq/openhermes-mistral-dpo-gptq

SGLang

How to use thobuiq/openhermes-mistral-dpo-gptq 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 "thobuiq/openhermes-mistral-dpo-gptq" \
    --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": "thobuiq/openhermes-mistral-dpo-gptq",
		"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 "thobuiq/openhermes-mistral-dpo-gptq" \
        --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": "thobuiq/openhermes-mistral-dpo-gptq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thobuiq/openhermes-mistral-dpo-gptq with Docker Model Runner:
```
docker model run hf.co/thobuiq/openhermes-mistral-dpo-gptq
```

openhermes-mistral-dpo-gptq

This model is a fine-tuned version of TheBloke/OpenHermes-2-Mistral-7B-GPTQ on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5952
Rewards/chosen: 0.1959
Rewards/rejected: -0.1959
Rewards/accuracies: 0.8125
Rewards/margins: 0.3918
Logps/rejected: -225.0688
Logps/chosen: -260.4842
Logits/rejected: -2.6980
Logits/chosen: -2.6353

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
training_steps: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6829	0.01	10	0.6352	0.0696	-0.0459	0.5625	0.1156	-223.5694	-261.7468	-2.6848	-2.6255
0.7018	0.01	20	0.6071	0.1534	-0.1323	0.5625	0.2857	-224.4329	-260.9089	-2.6932	-2.6321
0.6391	0.01	30	0.5922	0.2186	-0.1600	0.8125	0.3787	-224.7106	-260.2570	-2.6988	-2.6359
0.5993	0.02	40	0.5937	0.1966	-0.1896	0.8125	0.3861	-225.0059	-260.4774	-2.6987	-2.6355
0.6781	0.03	50	0.5952	0.1959	-0.1959	0.8125	0.3918	-225.0688	-260.4842	-2.6980	-2.6353

Framework versions

Transformers 4.35.2
Pytorch 2.0.1+cu117
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 3

Safetensors

Model size

3.41M params

Tensor type

F32

Model tree for thobuiq/openhermes-mistral-dpo-gptq

Base model

mistralai/Mistral-7B-v0.1

Finetuned

teknium/OpenHermes-2-Mistral-7B

Quantized

TheBloke/OpenHermes-2-Mistral-7B-GPTQ

Finetuned

(25)

this model