Instructions to use argilla/zephyr-7b-spin-iter0-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use argilla/zephyr-7b-spin-iter0-v0 with Transformers:

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

pipe = pipeline("text-generation", model="argilla/zephyr-7b-spin-iter0-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("argilla/zephyr-7b-spin-iter0-v0")
model = AutoModelForCausalLM.from_pretrained("argilla/zephyr-7b-spin-iter0-v0")
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 argilla/zephyr-7b-spin-iter0-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "argilla/zephyr-7b-spin-iter0-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "argilla/zephyr-7b-spin-iter0-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/argilla/zephyr-7b-spin-iter0-v0

SGLang

How to use argilla/zephyr-7b-spin-iter0-v0 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 "argilla/zephyr-7b-spin-iter0-v0" \
    --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": "argilla/zephyr-7b-spin-iter0-v0",
		"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 "argilla/zephyr-7b-spin-iter0-v0" \
        --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": "argilla/zephyr-7b-spin-iter0-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use argilla/zephyr-7b-spin-iter0-v0 with Docker Model Runner:
```
docker model run hf.co/argilla/zephyr-7b-spin-iter0-v0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

zephyr-7b-spin-iter0-v0

This model is a fine-tuned model with SPIN starting with alignment-handbook/zephyr-7b-sft-full on the argilla/10k_prompts_SPIN_iter0_zephyr_top dataset.

It achieves the following results on the evaluation set:

Loss: 0.2359
Rewards/real: 1.3255
Rewards/generated: -0.8966
Rewards/accuracies: 0.9792
Rewards/margins: 2.2221
Logps/generated: -309.8145
Logps/real: -304.9670
Logits/generated: -2.7558
Logits/real: -2.7547

MT-Bench results

Model	1st Turn Score	2nd Turn Score	Average Score
zephyr-7b-sft-full	6.6625	6.0250	6.34375
zephyr-7b-spin-iter0-v0	6.64375	6.1750	6.409375
zephyr-7b-spin-iter1-v0	6.90625	6.3000	6.603125
zephyr-7b-spin-iter2-v0	7.1375	6.3125	6.725000
zephyr-7b-spin-iter3-v0	7.09375	6.4500	6.771875

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

SPIN

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: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/real	Rewards/generated	Rewards/accuracies	Rewards/margins	Logps/generated	Logps/real	Logits/generated	Logits/real
0.3011	0.96	25	0.2442	1.1606	-0.9851	0.9792	2.1457	-310.6989	-306.6157	-2.7644	-2.7641
0.0376	1.92	50	0.2359	1.3255	-0.8966	0.9792	2.2221	-309.8145	-304.9670	-2.7558	-2.7547