--- language: - en license: other tags: - causal-lm datasets: - HuggingFaceH4/ultrachat_200k - allenai/ultrafeedback_binarized_cleaned - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - Intel/orca_dpo_pairs - hkust-nlp/deita-10k-v0 extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox model-index: - name: stablelm-2-zephyr-1_6b 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: 43.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-2-zephyr-1_6b 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: 69.3 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-2-zephyr-1_6b 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: 42.03 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-2-zephyr-1_6b 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: 45.11 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-2-zephyr-1_6b 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: 64.48 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-2-zephyr-1_6b 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: 35.33 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-2-zephyr-1_6b name: Open LLM Leaderboard --- # `StableLM 2 Zephyr 1.6B` ## Model Description `Stable LM 2 Zephyr 1.6B` is a 1.6 billion parameter instruction tuned language model inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). ## Usage `StableLM 2 Zephyr 1.6B` uses the following instruction format: ``` <|user|> Which famous math number begins with 1.6 ...?<|endoftext|> <|assistant|> The number you are referring to is 1.618033988749895. This is the famous value known as the golden ratio<|endoftext|> ``` This format is also available through the tokenizer's `apply_chat_template` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b') model = AutoModelForCausalLM.from_pretrained( 'stabilityai/stablelm-2-zephyr-1_6b', device_map="auto" ) prompt = [{'role': 'user', 'content': 'Which famous math number begins with 1.6 ...?'}] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to(model.device), max_new_tokens=1024, temperature=0.5, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ``` ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `StableLM 2 Zephyr 1.6B` model is an auto-regressive language model based on the transformer decoder architecture. * **Language(s)**: English * **Paper**: [Stable LM 2 1.6B Technical Report](https://drive.google.com/file/d/1JYJHszhS8EFChTbNAf8xmqhKjogWRrQF/view?usp=sharing) * **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git) * **Finetuned from model**: [https://huggingface.co/stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b) * **License**: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b/blob/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more. * **Contact**: For questions and comments about the model, please email `lm@stability.ai` ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): 1. SFT Datasets - HuggingFaceH4/ultrachat_200k - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Open-Orca/SlimOrca - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - hkust-nlp/deita-10k-v0 2. Preference Datasets: - allenai/ultrafeedback_binarized_cleaned - Intel/orca_dpo_pairs ## Performance ### MT-Bench mt_bench_plot | Model | Size | MT-Bench | |-------------------------|------|----------| | Mistral-7B-Instruct-v0.2| 7B | 7.61 | | Llama2-Chat | 70B | 6.86 | | stablelm-zephyr-3b | 3B | 6.64 | | MPT-30B-Chat | 30B | 6.39 | | **stablelm-2-zephyr-1.6b** | 1.6B | 5.42 | | Falcon-40B-Instruct | 40B | 5.17 | | Qwen-1.8B-Chat | 1.8B | 4.95 | | dolphin-2.6-phi-2 | 2.7B | 4.93 | | phi-2 | 2.7B | 4.29 | | TinyLlama-1.1B-Chat-v1.0| 1.1B | 3.46 | ### OpenLLM Leaderboard | Model | Size | Average | ARC Challenge (acc_norm) | HellaSwag (acc_norm) | MMLU (acc_norm) | TruthfulQA (mc2) | Winogrande (acc) | Gsm8k (acc) | |----------------------------------------|------|---------|-------------------------|----------------------|-----------------|------------------|------------------|-------------| | microsoft/phi-2 | 2.7B | 61.32% | 61.09% | 75.11% | 58.11% | 44.47% | 74.35% | 54.81% | | **stabilityai/stablelm-2-zephyr-1_6b** | 1.6B | 49.89% | 43.69% | 69.34% | 41.85% | 45.21% | 64.09% | 35.18% | | microsoft/phi-1_5 | 1.3B | 47.69% | 52.90% | 63.79% | 43.89% | 40.89% | 72.22% | 12.43% | | stabilityai/stablelm-2-1_6b | 1.6B | 45.54% | 43.43% | 70.49% | 38.93% | 36.65% | 65.90% | 17.82% | | mosaicml/mpt-7b | 7B | 44.28% | 47.70% | 77.57% | 30.80% | 33.40% | 72.14% | 4.02% | | KnutJaegersberg/Qwen-1_8B-Llamaified* | 1.8B | 44.75% | 37.71% | 58.87% | 46.37% | 39.41% | 61.72% | 24.41% | | openlm-research/open_llama_3b_v2 | 3B | 40.28% | 40.27% | 71.60% | 27.12% | 34.78% | 67.01% | 0.91% | | iiuae/falcon-rw-1b | 1B | 37.07% | 35.07% | 63.56% | 25.28% | 35.96% | 62.04% | 0.53% | | TinyLlama/TinyLlama-1.1B-3T | 1.1B | 36.40% | 33.79% | 60.31% | 26.04% | 37.32% | 59.51% | 1.44% | ### Training Infrastructure * **Hardware**: `StableLM 2 Zephyr 1.6B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training. ## Use and Limitations ### Intended Use The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about [safety and limitations](#limitations-and-bias) below. ### Limitations and Bias ​ This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @misc{StableLM-2-1.6B, url={[https://huggingface.co/stabilityai/stablelm-2-1.6b](https://huggingface.co/stabilityai/stablelm-2-1.6b)}, title={Stable LM 2 1.6B}, author={Stability AI Language Team} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_stabilityai__stablelm-2-zephyr-1_6b) | Metric |Value| |---------------------------------|----:| |Avg. |49.99| |AI2 Reasoning Challenge (25-Shot)|43.69| |HellaSwag (10-Shot) |69.30| |MMLU (5-Shot) |42.03| |TruthfulQA (0-shot) |45.11| |Winogrande (5-shot) |64.48| |GSM8k (5-shot) |35.33|