Instructions to use decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP with Transformers:

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

pipe = pipeline("text-generation", model="decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP")
model = AutoModelForCausalLM.from_pretrained("decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP

SGLang

How to use decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP 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 "decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP" \
    --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": "decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP",
		"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 "decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP" \
        --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": "decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP with Docker Model Runner:
```
docker model run hf.co/decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for decruz07/kellemar-DPO-Orca-Distilled-7B

This model was created using mlabonne/Marcoro14-7B-slerp as the base, and finetuned with argilla/distilabel-intel-orca-dpo-pairs

Model Details

Finetuned with these specific parameters: Steps: 200 Learning Rate: 5e5 Beta: 0.1

Model Description

Developed by: @decruz
Funded by [optional]: my full-time job
Finetuned from model [optional]: mlabonne/Marcoro14-7B-slerp

Benchmarks

Top 5 in OpenLLM Benchmarks as of 2024/01/17

OpenLLM

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
kellemar-DPO-Orca-Distilled-7B-SLERP	73.71	70.48	87.56	65.33	64.97	81.93	72.02

Nous

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
kellemar-DPO-Orca-Distilled-7B-SLERP	45.27	76.42	65.48	47.21	58.6
Marcoro14-7B-slerp	44.66	76.24	64.15	45.64	57.67
kellemar-DPO-Orca-Distilled-7B	43.61	73.14	55.73	42.28	53.69
kellemar-Orca-DPO-7B	43.35	73.43	54.02	42.24	53.26
OpenHermes-2.5-Mistral-7B	43.07	73.12	53.04	40.96	52.38

Uses

You can use this for basic inference. You could probably finetune with this if you want to.

How to Get Started with the Model

You can create a space out of this, or use basic python code to call the model directly and make inferences to it.

[More Information Needed]

Training Details

The following was used: `training_args = TrainingArguments( per_device_train_batch_size=4, gradient_accumulation_steps=4, gradient_checkpointing=True, learning_rate=5e-5, lr_scheduler_type="cosine", max_steps=200, save_strategy="no", logging_steps=1, output_dir=new_model, optim="paged_adamw_32bit", warmup_steps=100, bf16=True, report_to="wandb", )

Create DPO trainer

dpo_trainer = DPOTrainer( model, ref_model, args=training_args, train_dataset=dataset, tokenizer=tokenizer, peft_config=peft_config, beta=0.1, max_prompt_length=1024, max_length=1536, )`

Training Data

This was trained with https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs

Training Procedure

Trained with Labonne's Google Colab Notebook on Finetuning Mistral 7B with DPO.

Model Card Authors [optional]

@decruz

Model Card Contact

@decruz on X/Twitter

Downloads last month: 8

Safetensors

Model size

7B params

Tensor type

F16

Model tree for decruz07/kellemar-DPO-Orca-Distilled-7B-SLERP

Base model

mlabonne/Marcoro14-7B-slerp

Finetuned

(16)

this model

Merges

7 models

Quantizations

2 models

decruz07
/

kellemar-DPO-Orca-Distilled-7B-SLERP