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

Libraries

How to use decruz07/kellemar-DPO-7B-e with Transformers:

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

pipe = pipeline("text-generation", model="decruz07/kellemar-DPO-7B-e")
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-e")
model = AutoModelForCausalLM.from_pretrained("decruz07/kellemar-DPO-7B-e")
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

vLLM

How to use decruz07/kellemar-DPO-7B-e 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-e"
# 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-e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use decruz07/kellemar-DPO-7B-e 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-e" \
    --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-e",
		"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-e" \
        --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-e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Model Card for decruz07/kellemar-DPO-7B-e

Learning Rate: 5e-5, steps 300

Model Details

Created with beta = 0.05

Model Description

Developed by: @decruz
Funded by [optional]: my full-time job
Finetuned from model [optional]: teknium/OpenHermes-2.5-Mistral-7B

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: 3

Safetensors

Model size

7B params

Tensor type

F16