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

Libraries

How to use statking/zephyr-7b-sft-full-orpo with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("statking/zephyr-7b-sft-full-orpo")
model = AutoModelForCausalLM.from_pretrained("statking/zephyr-7b-sft-full-orpo", 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 statking/zephyr-7b-sft-full-orpo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/statking/zephyr-7b-sft-full-orpo

SGLang

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

Docker Model Runner
How to use statking/zephyr-7b-sft-full-orpo with Docker Model Runner:
```
docker model run hf.co/statking/zephyr-7b-sft-full-orpo
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

zephyr-7b-sft-full-orpo

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.4701
Rewards/chosen: -0.0364
Rewards/rejected: -0.0499
Rewards/accuracies: 0.6587
Rewards/margins: 0.0135
Logps/rejected: -0.9978
Logps/chosen: -0.7282
Logits/rejected: -2.9263
Logits/chosen: -2.9434
Nll Loss: 0.4357
Log Odds Ratio: -0.6093
Log Odds Chosen: 0.4456

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: 7e-06
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: inverse_sqrt
lr_scheduler_warmup_steps: 100
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen	Nll Loss	Log Odds Ratio	Log Odds Chosen
0.5226	0.1049	100	0.5280	-0.0386	-0.0472	0.6329	0.0086	-0.9448	-0.7728	-2.7583	-2.7860	0.4953	-0.6326	0.2873
0.5074	0.2098	200	0.5134	-0.0381	-0.0478	0.6409	0.0098	-0.9566	-0.7612	-2.6736	-2.7002	0.4774	-0.6357	0.3190
0.5265	0.3146	300	0.5012	-0.0379	-0.0479	0.6329	0.0099	-0.9572	-0.7588	-2.7317	-2.7594	0.4653	-0.6374	0.3278
0.5194	0.4195	400	0.4912	-0.0371	-0.0478	0.6429	0.0107	-0.9559	-0.7417	-2.6640	-2.6974	0.4560	-0.6284	0.3607
0.5008	0.5244	500	0.4847	-0.0373	-0.0489	0.6508	0.0117	-0.9786	-0.7455	-2.5957	-2.6294	0.4499	-0.6209	0.3873
0.4725	0.6293	600	0.4794	-0.0362	-0.0470	0.6349	0.0107	-0.9394	-0.7248	-2.6147	-2.6477	0.4435	-0.6320	0.3567
0.4875	0.7341	700	0.4767	-0.0368	-0.0498	0.6409	0.0129	-0.9955	-0.7365	-2.6910	-2.7213	0.4416	-0.6158	0.4180
0.4796	0.8390	800	0.4740	-0.0371	-0.0508	0.6508	0.0137	-1.0162	-0.7416	-2.7913	-2.8114	0.4396	-0.6169	0.4363
0.4851	0.9439	900	0.4714	-0.0357	-0.0466	0.6528	0.0109	-0.9324	-0.7143	-2.9543	-2.9692	0.4361	-0.6245	0.3669

Framework versions

Transformers 4.41.0.dev0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for statking/zephyr-7b-sft-full-orpo

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(942)

this model

Quantizations

1 model

statking
/

zephyr-7b-sft-full-orpo

zephyr-7b-sft-full-orpo

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for statking/zephyr-7b-sft-full-orpo

Dataset used to train statking/zephyr-7b-sft-full-orpo

Spaces using statking/zephyr-7b-sft-full-orpo 9