Instructions to use eren23/OGNO-7b-dpo-truthful with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eren23/OGNO-7b-dpo-truthful with Transformers:

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

pipe = pipeline("text-generation", model="eren23/OGNO-7b-dpo-truthful")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eren23/OGNO-7b-dpo-truthful")
model = AutoModelForCausalLM.from_pretrained("eren23/OGNO-7b-dpo-truthful", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eren23/OGNO-7b-dpo-truthful with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eren23/OGNO-7b-dpo-truthful"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eren23/OGNO-7b-dpo-truthful",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/eren23/OGNO-7b-dpo-truthful

SGLang

How to use eren23/OGNO-7b-dpo-truthful 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 "eren23/OGNO-7b-dpo-truthful" \
    --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": "eren23/OGNO-7b-dpo-truthful",
		"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 "eren23/OGNO-7b-dpo-truthful" \
        --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": "eren23/OGNO-7b-dpo-truthful",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use eren23/OGNO-7b-dpo-truthful with Docker Model Runner:
```
docker model run hf.co/eren23/OGNO-7b-dpo-truthful
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DPO Finetuned paulml/OGNO-7B using jondurbin/truthy-dpo-v0.1

paulml/OGNO-7B is a mistral 7b variant afaik and this repo is an experimental repo, so might not be useable in prod

Thx for the great data sources.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	76.14
AI2 Reasoning Challenge (25-Shot)	72.95
HellaSwag (10-Shot)	89.02
MMLU (5-Shot)	64.61
TruthfulQA (0-shot)	76.61
Winogrande (5-shot)	84.69
GSM8k (5-shot)	68.99

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Dataset used to train eren23/OGNO-7b-dpo-truthful

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Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

72.950
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

89.020
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.610
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

76.610
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

84.690
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

68.990