Instructions to use InferenceIllusionist/Excalibur-7b-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InferenceIllusionist/Excalibur-7b-DPO with Transformers:

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

pipe = pipeline("text-generation", model="InferenceIllusionist/Excalibur-7b-DPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("InferenceIllusionist/Excalibur-7b-DPO")
model = AutoModelForCausalLM.from_pretrained("InferenceIllusionist/Excalibur-7b-DPO")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InferenceIllusionist/Excalibur-7b-DPO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/InferenceIllusionist/Excalibur-7b-DPO

SGLang

How to use InferenceIllusionist/Excalibur-7b-DPO 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 "InferenceIllusionist/Excalibur-7b-DPO" \
    --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": "InferenceIllusionist/Excalibur-7b-DPO",
		"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 "InferenceIllusionist/Excalibur-7b-DPO" \
        --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": "InferenceIllusionist/Excalibur-7b-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Excalibur-7b-DPO

An initial foray into the world of fine-tuning. The goal of this release was to amplify the quality of the original model's responses, in particular for vision use cases*

Weighted (Importance Matrix) Quants available here

Static (Legacy) quants available here

Notes & Methodology

Excalibur-7b fine-tuned with Direct Preference Optimization (DPO) using Intel/orca_dpo_pairs
This is a quick experiment to determine the impact of DPO finetuning on the Excelsior-7b base model
Ran for a little over an hour on a single A100
Fine-tuning succeeded in making model conversational and more well-rounded
Benchmark scores increased in the following categories versus base Excelsior-7b:
- ARC: 69.71 -> 70.9
- HellaSwag: 87.56 -> 87.93
- TruthfulQA: 67.24 -> 70.82
- Average: 73.6 -> 73.84
Precision: bfloat16

Sample Question - Vision

*Requires additional mmproj file. You have two options for vision functionality (available inside this repo):

Select the gguf file of your choice in Koboldcpp as usual, then make sure to choose the mmproj file above in the LLaVA mmproj field of the model submenu:

Prompt Format

For best results please use ChatML for the prompt format. Alpaca may also work.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.84
AI2 Reasoning Challenge (25-Shot)	70.90
HellaSwag (10-Shot)	87.93
MMLU (5-Shot)	65.46
TruthfulQA (0-shot)	70.82
Winogrande (5-shot)	82.48
GSM8k (5-shot)	65.43