datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
- meta-math/MetaMathQA
- WizardLM/WizardLM_evol_instruct_V2_196k
- Intel/orca_dpo_pairs
language:
- en
tags:
- causal-lm
extra_gated_fields:
Name: text
Email: text
Country: text
Organization or Affiliation: text
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license: other
StableLM Zephyr 3B
Model Description
StableLM Zephyr 3B is a 3 billion parameter instruction tuned inspired by HugginFaceH4's Zephyr 7B training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using Direct Preference Optimization (DPO), evaluation for this model based on
MT Bench and Alpaca Benchmark
Usage
StableLM Zephyr 3B uses the following instruction format:
<|user|>
List 3 synonyms for the word "tiny"<|endoftext|>
<|assistant|>
1. Dwarf
2. Little
3. Petite<|endoftext|>
This format is also available through the tokenizer's apply_chat_template method:
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-zephyr-3b')
model = AutoModelForCausalLM.from_pretrained(
'stabilityai/stablelm-zephyr-3b',
trust_remote_code=True,
device_map="auto"
)
prompt = [{'role': 'user', 'content': 'List 3 synonyms for the word "tiny"'}]
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=0.8,
do_sample=True
)
print(tokenizer.decode(tokens[0], skip_special_tokens=False))
Model Details
- Developed by: Stability AI
- Model type:
StableLM Zephyr 3Bmodel is an auto-regressive language model based on the transformer decoder architecture. - Language(s): English
- Library: Alignment Handbook
- Finetuned from model: stabilityai/stablelm-3b-4e1t
- License: StabilityAI Non-Commercial Research Community License
- 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:
- SFT Datasets
- HuggingFaceH4/ultrachat_200k
- meta-math/MetaMathQA
- WizardLM/WizardLM_evol_instruct_V2_196k
- Open-Orca/SlimOrca
- Preference Datasets:
- HuggingFaceH4/ultrafeedback_binarized
- Intel/orca_dpo_pairs
Performance
MT-Bench and Alpaca Bench
| Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
|---|---|---|---|---|
| StableLM Zephyr 3B 🪁 | 3B | DPO | 6.64 | 76.00 |
| StableLM Zephyr (SFT only) | 3B | SFT | 6.04 | 71.15 |
| Capybara v1.9 | 3B | dSFT | 5.94 | - |
| MPT-Chat | 7B | dSFT | 5.42 | - |
| Xwin-LM v0.1 | 7B | dPPO | 6.19 | 87.83 |
| Mistral-Instruct v0.1 | 7B | - | 6.84 | - |
| Zephyr-7b-α | 7B | dDPO | 6.88 | - |
| Zephyr-7b-β | 7B | dDPO | 7.34 | 90.60 |
| Falcon-Instruct | 40B | dSFT | 5.17 | 45.71 |
| Guanaco | 65B | SFT | 6.41 | 71.80 |
| Llama2-Chat | 70B | RLHF | 6.86 | 92.66 |
| Vicuna v1.3 | 33B | dSFT | 7.12 | 88.99 |
| WizardLM v1.0 | 70B | dSFT | 7.71 | - |
| Xwin-LM v0.1 | 70B | dPPO | - | 95.57 |
| GPT-3.5-turbo | - | RLHF | 7.94 | 89.37 |
| Claude 2 | - | RLHF | 8.06 | 91.36 |
| GPT-4 | - | RLHF | 8.99 | 95.28 |
Other benchmark:
HuggingFace OpenLLM Leaderboard
Metric Value ARC (25-shot) 47.0 HellaSwag (10-shot) 74.2 MMLU (5-shot) 46.3 TruthfulQA (0-shot) 46.5 Winogrande (5-shot) 65.5 GSM8K (5-shot) 42.3 BigBench:
- Average: 35.26
- Details:
| Task | Version | Metric | Value | Stderr |
|---|---|---|---|---|
| bigbench_causal_judgement | 0 | multiple_choice_grade | 0.5316 | 0.0363 |
| bigbench_date_understanding | 0 | multiple_choice_grade | 0.4363 | 0.0259 |
| bigbench_disambiguation_qa | 0 | multiple_choice_grade | 0.3217 | 0.0291 |
| bigbench_dyck_languages | 0 | multiple_choice_grade | 0.1450 | 0.0111 |
| bigbench_formal_fallacies_syllogisms_negation | 0 | multiple_choice_grade | 0.4982 | 0.0042 |
| bigbench_geometric_shapes | 0 | multiple_choice_grade | 0.1086 | 0.0164 |
| bigbench_hyperbaton | 0 | exact_str_match | 0.0000 | 0.0000 |
| bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade | 0.5232 | 0.0022 |
| bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade | 0.2480 | 0.0193 |
| bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade | 0.1814 | 0.0146 |
| bigbench_movie_recommendation | 0 | multiple_choice_grade | 0.4067 | 0.0284 |
| bigbench_navigate | 0 | multiple_choice_grade | 0.2580 | 0.0196 |
| bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade | 0.5990 | 0.0155 |
| bigbench_ruin_names | 0 | multiple_choice_grade | 0.4370 | 0.0111 |
| bigbench_salient_translation_error_detection | 0 | multiple_choice_grade | 0.3951 | 0.0231 |
| bigbench_snarks | 0 | multiple_choice_grade | 0.2265 | 0.0133 |
| bigbench_sports_understanding | 0 | multiple_choice_grade | 0.6464 | 0.0356 |
| bigbench_temporal_sequences | 0 | multiple_choice_grade | 0.5091 | 0.0159 |
| bigbench_tracking_shuffled_objects_five_objects | 0 | multiple_choice_grade | 0.2680 | 0.0140 |
| bigbench_tracking_shuffled_objects_seven_objects | 0 | multiple_choice_grade | 0.1856 | 0.0110 |
| bigbench_tracking_shuffled_objects_three_objects | 0 | multiple_choice_grade | 0.1269 | 0.0080 |
- AGI Benchmark:
- Average: 33.23
- Details: | Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:| |agieval_aqua_rat | 0|acc |0.2126|± |0.0257| | | |acc_norm|0.1890|± |0.0246| |agieval_gaokao_biology | 0|acc |0.2571|± |0.0302| | | |acc_norm|0.3143|± |0.0321| |agieval_gaokao_chemistry | 0|acc |0.2464|± |0.0300| | | |acc_norm|0.2899|± |0.0316| |agieval_gaokao_chinese | 0|acc |0.2927|± |0.0291| | | |acc_norm|0.3049|± |0.0294| |agieval_gaokao_english | 0|acc |0.6176|± |0.0278| | | |acc_norm|0.6438|± |0.0274| |agieval_gaokao_geography | 0|acc |0.3015|± |0.0326| | | |acc_norm|0.3065|± |0.0328| |agieval_gaokao_history | 0|acc |0.3106|± |0.0303| | | |acc_norm|0.3319|± |0.0308| |agieval_gaokao_mathqa | 0|acc |0.2650|± |0.0236| | | |acc_norm|0.2707|± |0.0237| |agieval_gaokao_physics | 0|acc |0.3450|± |0.0337| | | |acc_norm|0.3550|± |0.0339| |agieval_logiqa_en | 0|acc |0.2980|± |0.0179| | | |acc_norm|0.3195|± |0.0183| |agieval_logiqa_zh | 0|acc |0.2842|± |0.0177| | | |acc_norm|0.3318|± |0.0185| |agieval_lsat_ar | 0|acc |0.2000|± |0.0264| | | |acc_norm|0.2043|± |0.0266| |agieval_lsat_lr | 0|acc |0.3176|± |0.0206| | | |acc_norm|0.3275|± |0.0208| |agieval_lsat_rc | 0|acc |0.4312|± |0.0303| | | |acc_norm|0.4201|± |0.0301| |agieval_sat_en | 0|acc |0.6117|± |0.0340| | | |acc_norm|0.6117|± |0.0340| |agieval_sat_en_without_passage| 0|acc |0.3398|± |0.0331| | | |acc_norm|0.3495|± |0.0333| |agieval_sat_math | 0|acc |0.3182|± |0.0315| | | |acc_norm|0.2909|± |0.0307|
Training Infrastructure
- Hardware:
StableLM Zephyr 3Bwas 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 for DPO training.
Use and Limitations
Intended Use
The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications.
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 internal testing, we discovered that while the model will not output harmful information if not prompted to do so, it is 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.