Instructions to use decruz07/kellemar-DPO-7B-v1.01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use decruz07/kellemar-DPO-7B-v1.01 with Transformers:

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

pipe = pipeline("text-generation", model="decruz07/kellemar-DPO-7B-v1.01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("decruz07/kellemar-DPO-7B-v1.01")
model = AutoModelForCausalLM.from_pretrained("decruz07/kellemar-DPO-7B-v1.01", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use decruz07/kellemar-DPO-7B-v1.01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "decruz07/kellemar-DPO-7B-v1.01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decruz07/kellemar-DPO-7B-v1.01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/decruz07/kellemar-DPO-7B-v1.01

SGLang

How to use decruz07/kellemar-DPO-7B-v1.01 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-7B-v1.01" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decruz07/kellemar-DPO-7B-v1.01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-7B-v1.01" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "decruz07/kellemar-DPO-7B-v1.01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use decruz07/kellemar-DPO-7B-v1.01 with Docker Model Runner:
```
docker model run hf.co/decruz07/kellemar-DPO-7B-v1.01
```

Model Card for decruz07/kellemar-DPO-7B-v1.01

This model was created using OpenHermes-2.5 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]: teknium/OpenHermes-2.5-Mistral-7B

Benchmarks

OpenLLM

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
68.32	65.78	85.04	63.24	55.54	78.69	61.64

Nous

AGIEval	GPT4All	TruthfulQA	Bigbench	Average
43.17	73.25	55.87	42.2	53.62

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-7B-v1.01

Base model

mistralai/Mistral-7B-v0.1

Finetuned

teknium/OpenHermes-2.5-Mistral-7B