Instructions to use UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 with Transformers:

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

pipe = pipeline("text-generation", model="UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1")
model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1", 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 UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1

SGLang

How to use UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 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 "UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1" \
    --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": "UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1",
		"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 "UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1" \
        --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": "UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 with Docker Model Runner:
```
docker model run hf.co/UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1
```

zephyr-7b-sft-full-SPIN-iter1 / README.md

leaderboard-pr-bot

Adding Evaluation Results

3fc09e3 verified over 2 years ago

5.54 kB

language:
  - en
license: mit
datasets:
  - UCLA-AGI/SPIN_iter1
base_model: alignment-handbook/zephyr-7b-sft-full
pipeline_tag: text-generation
model-index:
  - name: zephyr-7b-sft-full-SPIN-iter1
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 65.87
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 85.44
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 60.95
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 57.39
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 76.64
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 30.86
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1
          name: Open LLM Leaderboard

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models (https://arxiv.org/abs/2401.01335)

zephyr-7b-sft-full-spin-iter1

This model is a self-play fine-tuned model at iteration 1 from alignment-handbook/zephyr-7b-sft-full using synthetic data based on on the HuggingFaceH4/ultrachat_200k dataset.

Model Details

Model Description

Model type: A 7B parameter GPT-like model fine-tuned on synthetic datasets.
Language(s) (NLP): Primarily English
License: MIT
Finetuned from model: alignment-handbook/zephyr-7b-sft-full (based on mistralai/Mistral-7B-v0.1)

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
optimizer: RMSProp
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	62.86
ARC (25-shot)	65.87
HellaSwag (10-shot)	85.44
MMLU (5-shot)	60.95
TruthfulQA (0-shot)	57.39
Winogrande (5-shot)	76.64
GSM8K (5-shot)	30.86

Citation

@misc{chen2024selfplay,
      title={Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models}, 
      author={Zixiang Chen and Yihe Deng and Huizhuo Yuan and Kaixuan Ji and Quanquan Gu},
      year={2024},
      eprint={2401.01335},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	62.86
AI2 Reasoning Challenge (25-Shot)	65.87
HellaSwag (10-Shot)	85.44
MMLU (5-Shot)	60.95
TruthfulQA (0-shot)	57.39
Winogrande (5-shot)	76.64
GSM8k (5-shot)	30.86