Instructions to use ishant0121/zephyr-7b-dpo-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ishant0121/zephyr-7b-dpo-full with Transformers:

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

pipe = pipeline("text-generation", model="ishant0121/zephyr-7b-dpo-full")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ishant0121/zephyr-7b-dpo-full")
model = AutoModelForCausalLM.from_pretrained("ishant0121/zephyr-7b-dpo-full")
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 ishant0121/zephyr-7b-dpo-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ishant0121/zephyr-7b-dpo-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ishant0121/zephyr-7b-dpo-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ishant0121/zephyr-7b-dpo-full

SGLang

How to use ishant0121/zephyr-7b-dpo-full 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 "ishant0121/zephyr-7b-dpo-full" \
    --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": "ishant0121/zephyr-7b-dpo-full",
		"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 "ishant0121/zephyr-7b-dpo-full" \
        --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": "ishant0121/zephyr-7b-dpo-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ishant0121/zephyr-7b-dpo-full with Docker Model Runner:
```
docker model run hf.co/ishant0121/zephyr-7b-dpo-full
```

zephyr-7b-dpo-full

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.4963
Rewards/chosen: -1.3619
Rewards/rejected: -2.3893
Rewards/accuracies: 0.7619
Rewards/margins: 1.0274
Logps/rejected: -499.1414
Logps/chosen: -418.1593
Logits/rejected: 1.1001
Logits/chosen: 0.2372

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: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.6267	0.1047	100	-2.4885	-2.4228	-298.8784	-298.6605	0.6219	0.7123	-0.1691	0.2154	-0.3845
0.5618	0.2093	200	-1.1643	-0.9025	-368.7673	-409.5043	0.5559	0.75	-0.8680	0.6250	-1.4930
0.5298	0.3140	300	0.1099	0.5848	-387.0007	-442.8027	0.5283	0.7679	-1.0503	0.7756	-1.8260
0.5585	0.4186	400	-1.2260	-0.6461	-368.9012	-419.9413	0.5200	0.7639	-0.8693	0.7280	-1.5973
0.5074	0.5233	500	0.1651	0.9655	-402.5899	-472.5687	0.5043	0.7698	-1.2062	0.9174	-2.1236
0.4678	0.6279	600	-0.1276	0.7528	-405.3750	-480.0774	0.4995	0.7698	-1.2341	0.9646	-2.1987
0.4767	0.7326	700	0.4974	-1.3438	-2.3581	0.7619	1.0142	-496.0146	-416.3518	1.1957	0.3567
0.475	0.8373	800	0.4971	-1.3985	-2.3889	0.7639	0.9904	-499.1005	-421.8184	1.0101	0.2102
0.4828	0.9419	900	0.4963	-1.3653	-2.3944	0.7639	1.0292	-499.6516	-418.4965	1.1202	0.2514

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1

Downloads last month: 1

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for ishant0121/zephyr-7b-dpo-full

Base model

mistralai/Mistral-7B-v0.1

Finetuned

alignment-handbook/zephyr-7b-sft-full

Finetuned

(393)

this model

ishant0121
/

zephyr-7b-dpo-full

zephyr-7b-dpo-full

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for ishant0121/zephyr-7b-dpo-full

Dataset used to train ishant0121/zephyr-7b-dpo-full