File size: 11,410 Bytes
4921c99 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 |
---
language:
- en
license: other
tags:
- causal-lm
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
- meta-math/MetaMathQA
- WizardLM/WizardLM_evol_instruct_V2_196k
- Intel/orca_dpo_pairs
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-zephyr-3b
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: 46.08
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
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: 74.16
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
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: 46.17
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
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: 46.49
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
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: 65.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
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: 42.15
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=stabilityai/stablelm-zephyr-3b
name: Open LLM Leaderboard
---
[](https://hf.co/QuantFactory)
# QuantFactory/stablelm-zephyr-3b-GGUF
This is quantized version of [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) created using llama.cpp
# Original Model Card
# `StableLM Zephyr 3B`
Please note: For commercial use, please refer to https://stability.ai/license.
## Model Description
`StableLM Zephyr 3B` is a 3 billion parameter instruction tuned inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290), evaluation for this model based on
[MT Bench](https://arxiv.org/abs/2306.05685) and [Alpaca Benchmark](https://tatsu-lab.github.io/alpaca_eval/)
## 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:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-zephyr-3b')
model = AutoModelForCausalLM.from_pretrained(
'stabilityai/stablelm-zephyr-3b',
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))
```
You can also see how to run a performance optimized version of this model [here](https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/273-stable-zephyr-3b-chatbot/273-stable-zephyr-3b-chatbot.ipynb) using [OpenVINO](https://docs.openvino.ai/2023.2/home.html) from Intel.
## Model Details
* **Developed by**: [Stability AI](https://stability.ai/)
* **Model type**: `StableLM Zephyr 3B` model is an auto-regressive language model based on the transformer decoder architecture.
* **Language(s)**: English
* **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git)
* **Finetuned from model**: [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)
* **License**: [StabilityAI Community License](https://huggingface.co/stabilityai/stablelm-zephyr-3b/raw/main/LICENSE.md).
* **Commercial License**: to use this model commercially, please refer to https://stability.ai/license
* **Contact**: For questions and comments about the model, please email `[email protected]`
### 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
2. Preference Datasets:
- HuggingFaceH4/ultrafeedback_binarized
- Intel/orca_dpo_pairs
## Performance
### MT-Bench and Alpaca Bench
<img src="https://cdn-uploads.huggingface.co/production/uploads/6310474ca119d49bc1eb0d80/8WIZS6dAlu5kSH-382pMl.png" alt="mt_bench_plot" width="600"/>
| 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 benchmarks:
| Task | 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 (Avg) | 35.26 |
| AGI Benchmark (Avg) | 33.23 |
### Training Infrastructure
* **Hardware**: `StableLM Zephyr 3B` 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.
## Commitment to Ethical AI
In line with our responsibility towards ethical AI development, `StableLM Zephyr 3B` is released with a focus on ensuring safety, reliability, and appropriateness in its applications. To this end, we have evaluated `StableLM Zephyr 3B` on 488 malicious prompts and used standard protocols to assess the harmfulness of its outputs. Compared to Zephyr-7b-β, `StableLM Zephyr 3B` reduces the number of harmful outputs as assessed by GPT-4 by 55. Additionally, we performed an internal red teaming event targeting the following abuse areas:
* **Self-Harm Methods**: (Suicide Methods, Encouragement of Self-Harm, Methods and encouragement of Eating Disorders)
* **Misinformation**: (Health, Conspiracy Theories, Social Unrest/Conflict, Political Misinformation, & Climate change)
* **Hate Speech**: (Race, Stereotypes, Immigrants, Gender, Personally Identifiable Information such as Social security numbers, Full names, ID numbers, Email addresses, and telephone numbers)
We have incorporated the findings of our malicious prompts evaluation and red teaming event into our release. Users are encouraged to fine-tune and evaluate the model to suit their specific needs, considering the potential biases and limitations found in `StableLM Zephyr 3B` and inherent in other LLM models.
## 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. For commercial use, please refer to https://stability.ai/membership.
### 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 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.
# [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-zephyr-3b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |53.43|
|AI2 Reasoning Challenge (25-Shot)|46.08|
|HellaSwag (10-Shot) |74.16|
|MMLU (5-Shot) |46.17|
|TruthfulQA (0-shot) |46.49|
|Winogrande (5-shot) |65.51|
|GSM8k (5-shot) |42.15|
|