Instructions to use sambar/zephyr-7b-ipo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sambar/zephyr-7b-ipo-lora with Transformers:

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

pipe = pipeline("text-generation", model="sambar/zephyr-7b-ipo-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sambar/zephyr-7b-ipo-lora")
model = AutoModelForCausalLM.from_pretrained("sambar/zephyr-7b-ipo-lora", 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 sambar/zephyr-7b-ipo-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sambar/zephyr-7b-ipo-lora

SGLang

How to use sambar/zephyr-7b-ipo-lora 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 "sambar/zephyr-7b-ipo-lora" \
    --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": "sambar/zephyr-7b-ipo-lora",
		"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 "sambar/zephyr-7b-ipo-lora" \
        --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": "sambar/zephyr-7b-ipo-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sambar/zephyr-7b-ipo-lora with Docker Model Runner:
```
docker model run hf.co/sambar/zephyr-7b-ipo-lora
```

zephyr-7b-ipo-lora

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 18.3397
Rewards/chosen: 0.0292
Rewards/rejected: -0.1006
Rewards/accuracies: 0.7200
Rewards/margins: 0.1298
Logps/rejected: -212.0379
Logps/chosen: -255.2319
Logits/rejected: -1.7967
Logits/chosen: -2.0243

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 32
total_train_batch_size: 256
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
19.3937	1.0	242	19.3450	0.0291	-0.0729	0.7040	0.1020	-211.7608	-255.2333	-1.7962	-2.0237
19.376	2.0	484	18.8198	0.0270	-0.0949	0.7020	0.1218	-211.9809	-255.2546	-1.7954	-2.0232
18.4503	3.0	726	18.3397	0.0292	-0.1006	0.7200	0.1298	-212.0379	-255.2319	-1.7967	-2.0243

Framework versions

Transformers 4.35.0
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.14.1

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