Instructions to use argilla/zephyr-7b-spin-iter3-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use argilla/zephyr-7b-spin-iter3-v0 with Transformers:

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

pipe = pipeline("text-generation", model="argilla/zephyr-7b-spin-iter3-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-iter3-v0")
model = AutoModelForCausalLM.from_pretrained("argilla/zephyr-7b-spin-iter3-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-iter3-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-iter3-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-iter3-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/argilla/zephyr-7b-spin-iter3-v0

SGLang

How to use argilla/zephyr-7b-spin-iter3-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-iter3-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-iter3-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-iter3-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-iter3-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use argilla/zephyr-7b-spin-iter3-v0 with Docker Model Runner:
```
docker model run hf.co/argilla/zephyr-7b-spin-iter3-v0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

zephyr-7b-spin-iter3-v0

A model matching the results of SPIN with very little data (30x less), carefully curated by the amazing Data Is Better Together community

This model is a fine-tuned version of argilla/zephyr-7b-spin-iter2-v0 on the argilla/10k_prompts_SPIN_iter3_zephyr_top and the argilla/10k_prompts_SPIN_iter2_zephyr_top dataset.

Check this repo for full reproducible code using the original SPIN implementation and distilabel.

If you want to contribute to high quality datasets like this, contribute to the DIBT prompt collective initiative.

MT-Bench results

Model	1st Turn Score	2nd Turn Score	Average Score	SPIN paper Score
zephyr-7b-sft-full	6.6625	6.0250	6.34375	5.94
zephyr-7b-spin-iter0-v0	6.64375	6.1750	6.409375	6.46
zephyr-7b-spin-iter1-v0	6.90625	6.3000	6.603125	6.65
zephyr-7b-spin-iter2-v0	7.1375	6.3125	6.725000	6.78
zephyr-7b-spin-iter3-v0	7.09375	6.4500	6.771875	-

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: 1e-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
0.2928	0.49	25	0.3951	-2.6212	-20.3268	0.9062	17.7056	-700.5638	-278.0876	-2.8098	-2.8090
0.1487	0.97	50	0.1319	-2.9077	-29.1459	0.9375	26.2382	-702.3276	-278.1449	-2.8218	-2.8066
0.006	1.46	75	0.1269	-2.6037	-29.1519	0.9583	26.5482	-702.3289	-278.0841	-2.8175	-2.8037
0.0086	1.94	100	0.1099	-2.9181	-29.6970	0.9271	26.7789	-702.4378	-278.1470	-2.8177	-2.8051