File size: 21,888 Bytes
8b19283 845d4c0 e63478c 8b19283 67560ab 8b19283 ff17311 8b19283 ff17311 8b19283 e63478c 8b19283 |
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 |
---
license: apache-2.0
datasets:
- teknium/OpenHermes-2.5
tags:
- axolotl
- 01-ai/Yi-1.5-9B-Chat
- finetune
---
# Hermes-2.5-Yi-1.5-9B-Chat
This model is a fine-tuned version of [01-ai/Yi-1.5-9B-Chat](https://huggingface.co./01-ai/Yi-1.5-9B-Chat) on the [teknium/OpenHermes-2.5](https://huggingface.co./datasets/teknium/OpenHermes-2.5) dataset.
I'm very happy with the results. The model now seems a lot smarter and "aware" in certain situations (first look, so I might change my opinion with more usage). It got quite an big edge on the AGIEval Benchmark for models in it's class.
I plan to extend its context length to 32k with POSE.
## Model Details
- **Base Model:** 01-ai/Yi-1.5-9B-Chat
- **chat-template:** chatml
- **Dataset:** teknium/OpenHermes-2.5
- **Sequence Length:** 8192 tokens
- **Training:**
- **Epochs:** 1
- **Hardware:** 4 Nodes x 4 NVIDIA A100 40GB GPUs
- **Duration:** 48:32:13
- **Cluster:** KIT SCC Cluster
## Benchmark n_shots=0
| Benchmark | Score |
|-------------------|--------|
| ARC (Challenge) | 52.47% |
| ARC (Easy) | 81.65% |
| BoolQ | 87.22% |
| HellaSwag | 60.52% |
| OpenBookQA | 33.60% |
| PIQA | 81.12% |
| Winogrande | 72.22% |
| AGIEval | 38.46% |
| TruthfulQA | 44.22% |
| MMLU | 59.72% |
| IFEval | 47.96% |
For detailed benchmark results, including sub-categories and various metrics, please refer to the [full benchmark table](#full-benchmark-results) at the end of this README.
## GGUF and Quantizations
- llama.cpp [b3166](https://github.com/ggerganov/llama.cpp/releases/tag/b3166)
- [juvi21/Hermes-2.5-Yi-1.5-9B-Chat-GGUF](https://huggingface.co./juvi21/Hermes-2.5-Yi-1.5-9B-Chat-GGUF) is availabe in:
- **F16** **Q8_0** **Q6_KQ5_K_M** **Q4_K_M** **Q3_K_M** **Q2_K**
## Usage
To use this model, you can load it using the Hugging Face Transformers library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("juvi21/Hermes-2.5-Yi-1.5-9B-Chat")
tokenizer = AutoTokenizer.from_pretrained("juvi21/Hermes-2.5-Yi-1.5-9B-Chat")
# Generate text
input_text = "What is the question to 42?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
## chatml
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
Knock Knock, who is there?<|im_end|>
<|im_start|>assistant
Hi there! <|im_end|>
```
## License
This model is released under the Apache 2.0 license.
## Acknowledgements
Special thanks to:
- Teknium for the great OpenHermes-2.5 dataset
- 01-ai for their great model
- KIT SCC for FLOPS
## Citation
If you use this model in your research, consider citing. Although definetly cite NousResearch and 01-ai:
```bibtex
@misc{
author = {juvi21},
title = Hermes-2.5-Yi-1.5-9B-Chat},
year = {2024},
}
```
## full-benchmark-results
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|---------------------------------------|-------|------|-----:|-----------------------|---|------:|---|------|
|agieval |N/A |none | 0|acc |↑ | 0.5381|± |0.0049|
| | |none | 0|acc_norm |↑ | 0.5715|± |0.0056|
| - agieval_aqua_rat | 1|none | 0|acc |↑ | 0.3858|± |0.0306|
| | |none | 0|acc_norm |↑ | 0.3425|± |0.0298|
| - agieval_gaokao_biology | 1|none | 0|acc |↑ | 0.6048|± |0.0338|
| | |none | 0|acc_norm |↑ | 0.6000|± |0.0339|
| - agieval_gaokao_chemistry | 1|none | 0|acc |↑ | 0.4879|± |0.0348|
| | |none | 0|acc_norm |↑ | 0.4106|± |0.0343|
| - agieval_gaokao_chinese | 1|none | 0|acc |↑ | 0.5935|± |0.0314|
| | |none | 0|acc_norm |↑ | 0.5813|± |0.0315|
| - agieval_gaokao_english | 1|none | 0|acc |↑ | 0.8235|± |0.0218|
| | |none | 0|acc_norm |↑ | 0.8431|± |0.0208|
| - agieval_gaokao_geography | 1|none | 0|acc |↑ | 0.7085|± |0.0323|
| | |none | 0|acc_norm |↑ | 0.6985|± |0.0326|
| - agieval_gaokao_history | 1|none | 0|acc |↑ | 0.7830|± |0.0269|
| | |none | 0|acc_norm |↑ | 0.7660|± |0.0277|
| - agieval_gaokao_mathcloze | 1|none | 0|acc |↑ | 0.0508|± |0.0203|
| - agieval_gaokao_mathqa | 1|none | 0|acc |↑ | 0.3761|± |0.0259|
| | |none | 0|acc_norm |↑ | 0.3590|± |0.0256|
| - agieval_gaokao_physics | 1|none | 0|acc |↑ | 0.4950|± |0.0354|
| | |none | 0|acc_norm |↑ | 0.4700|± |0.0354|
| - agieval_jec_qa_ca | 1|none | 0|acc |↑ | 0.6557|± |0.0150|
| | |none | 0|acc_norm |↑ | 0.5926|± |0.0156|
| - agieval_jec_qa_kd | 1|none | 0|acc |↑ | 0.7310|± |0.0140|
| | |none | 0|acc_norm |↑ | 0.6610|± |0.0150|
| - agieval_logiqa_en | 1|none | 0|acc |↑ | 0.5177|± |0.0196|
| | |none | 0|acc_norm |↑ | 0.4839|± |0.0196|
| - agieval_logiqa_zh | 1|none | 0|acc |↑ | 0.4854|± |0.0196|
| | |none | 0|acc_norm |↑ | 0.4501|± |0.0195|
| - agieval_lsat_ar | 1|none | 0|acc |↑ | 0.2913|± |0.0300|
| | |none | 0|acc_norm |↑ | 0.2696|± |0.0293|
| - agieval_lsat_lr | 1|none | 0|acc |↑ | 0.7196|± |0.0199|
| | |none | 0|acc_norm |↑ | 0.6824|± |0.0206|
| - agieval_lsat_rc | 1|none | 0|acc |↑ | 0.7212|± |0.0274|
| | |none | 0|acc_norm |↑ | 0.6989|± |0.0280|
| - agieval_math | 1|none | 0|acc |↑ | 0.0910|± |0.0091|
| - agieval_sat_en | 1|none | 0|acc |↑ | 0.8204|± |0.0268|
| | |none | 0|acc_norm |↑ | 0.8301|± |0.0262|
| - agieval_sat_en_without_passage | 1|none | 0|acc |↑ | 0.5194|± |0.0349|
| | |none | 0|acc_norm |↑ | 0.4806|± |0.0349|
| - agieval_sat_math | 1|none | 0|acc |↑ | 0.5864|± |0.0333|
| | |none | 0|acc_norm |↑ | 0.5409|± |0.0337|
|arc_challenge | 1|none | 0|acc |↑ | 0.5648|± |0.0145|
| | |none | 0|acc_norm |↑ | 0.5879|± |0.0144|
|arc_easy | 1|none | 0|acc |↑ | 0.8241|± |0.0078|
| | |none | 0|acc_norm |↑ | 0.8165|± |0.0079|
|boolq | 2|none | 0|acc |↑ | 0.8624|± |0.0060|
|hellaswag | 1|none | 0|acc |↑ | 0.5901|± |0.0049|
| | |none | 0|acc_norm |↑ | 0.7767|± |0.0042|
|ifeval | 2|none | 0|inst_level_loose_acc |↑ | 0.5156|± |N/A |
| | |none | 0|inst_level_strict_acc |↑ | 0.4748|± |N/A |
| | |none | 0|prompt_level_loose_acc |↑ | 0.3863|± |0.0210|
| | |none | 0|prompt_level_strict_acc|↑ | 0.3309|± |0.0202|
|mmlu |N/A |none | 0|acc |↑ | 0.6942|± |0.0037|
| - abstract_algebra | 0|none | 0|acc |↑ | 0.4900|± |0.0502|
| - anatomy | 0|none | 0|acc |↑ | 0.6815|± |0.0402|
| - astronomy | 0|none | 0|acc |↑ | 0.7895|± |0.0332|
| - business_ethics | 0|none | 0|acc |↑ | 0.7600|± |0.0429|
| - clinical_knowledge | 0|none | 0|acc |↑ | 0.7132|± |0.0278|
| - college_biology | 0|none | 0|acc |↑ | 0.8056|± |0.0331|
| - college_chemistry | 0|none | 0|acc |↑ | 0.5300|± |0.0502|
| - college_computer_science | 0|none | 0|acc |↑ | 0.6500|± |0.0479|
| - college_mathematics | 0|none | 0|acc |↑ | 0.4100|± |0.0494|
| - college_medicine | 0|none | 0|acc |↑ | 0.6763|± |0.0357|
| - college_physics | 0|none | 0|acc |↑ | 0.5000|± |0.0498|
| - computer_security | 0|none | 0|acc |↑ | 0.8200|± |0.0386|
| - conceptual_physics | 0|none | 0|acc |↑ | 0.7489|± |0.0283|
| - econometrics | 0|none | 0|acc |↑ | 0.5877|± |0.0463|
| - electrical_engineering | 0|none | 0|acc |↑ | 0.6759|± |0.0390|
| - elementary_mathematics | 0|none | 0|acc |↑ | 0.6481|± |0.0246|
| - formal_logic | 0|none | 0|acc |↑ | 0.5873|± |0.0440|
| - global_facts | 0|none | 0|acc |↑ | 0.3900|± |0.0490|
| - high_school_biology | 0|none | 0|acc |↑ | 0.8613|± |0.0197|
| - high_school_chemistry | 0|none | 0|acc |↑ | 0.6453|± |0.0337|
| - high_school_computer_science | 0|none | 0|acc |↑ | 0.8300|± |0.0378|
| - high_school_european_history | 0|none | 0|acc |↑ | 0.8182|± |0.0301|
| - high_school_geography | 0|none | 0|acc |↑ | 0.8485|± |0.0255|
| - high_school_government_and_politics| 0|none | 0|acc |↑ | 0.8964|± |0.0220|
| - high_school_macroeconomics | 0|none | 0|acc |↑ | 0.7923|± |0.0206|
| - high_school_mathematics | 0|none | 0|acc |↑ | 0.4407|± |0.0303|
| - high_school_microeconomics | 0|none | 0|acc |↑ | 0.8655|± |0.0222|
| - high_school_physics | 0|none | 0|acc |↑ | 0.5298|± |0.0408|
| - high_school_psychology | 0|none | 0|acc |↑ | 0.8679|± |0.0145|
| - high_school_statistics | 0|none | 0|acc |↑ | 0.6898|± |0.0315|
| - high_school_us_history | 0|none | 0|acc |↑ | 0.8873|± |0.0222|
| - high_school_world_history | 0|none | 0|acc |↑ | 0.8312|± |0.0244|
| - human_aging | 0|none | 0|acc |↑ | 0.7085|± |0.0305|
| - human_sexuality | 0|none | 0|acc |↑ | 0.7557|± |0.0377|
| - humanities |N/A |none | 0|acc |↑ | 0.6323|± |0.0067|
| - international_law | 0|none | 0|acc |↑ | 0.8099|± |0.0358|
| - jurisprudence | 0|none | 0|acc |↑ | 0.7685|± |0.0408|
| - logical_fallacies | 0|none | 0|acc |↑ | 0.7975|± |0.0316|
| - machine_learning | 0|none | 0|acc |↑ | 0.5179|± |0.0474|
| - management | 0|none | 0|acc |↑ | 0.8835|± |0.0318|
| - marketing | 0|none | 0|acc |↑ | 0.9017|± |0.0195|
| - medical_genetics | 0|none | 0|acc |↑ | 0.8000|± |0.0402|
| - miscellaneous | 0|none | 0|acc |↑ | 0.8225|± |0.0137|
| - moral_disputes | 0|none | 0|acc |↑ | 0.7283|± |0.0239|
| - moral_scenarios | 0|none | 0|acc |↑ | 0.4860|± |0.0167|
| - nutrition | 0|none | 0|acc |↑ | 0.7353|± |0.0253|
| - other |N/A |none | 0|acc |↑ | 0.7287|± |0.0077|
| - philosophy | 0|none | 0|acc |↑ | 0.7170|± |0.0256|
| - prehistory | 0|none | 0|acc |↑ | 0.7346|± |0.0246|
| - professional_accounting | 0|none | 0|acc |↑ | 0.5638|± |0.0296|
| - professional_law | 0|none | 0|acc |↑ | 0.5163|± |0.0128|
| - professional_medicine | 0|none | 0|acc |↑ | 0.6875|± |0.0282|
| - professional_psychology | 0|none | 0|acc |↑ | 0.7092|± |0.0184|
| - public_relations | 0|none | 0|acc |↑ | 0.6727|± |0.0449|
| - security_studies | 0|none | 0|acc |↑ | 0.7347|± |0.0283|
| - social_sciences |N/A |none | 0|acc |↑ | 0.7910|± |0.0072|
| - sociology | 0|none | 0|acc |↑ | 0.8060|± |0.0280|
| - stem |N/A |none | 0|acc |↑ | 0.6581|± |0.0081|
| - us_foreign_policy | 0|none | 0|acc |↑ | 0.8900|± |0.0314|
| - virology | 0|none | 0|acc |↑ | 0.5301|± |0.0389|
| - world_religions | 0|none | 0|acc |↑ | 0.8012|± |0.0306|
|openbookqa | 1|none | 0|acc |↑ | 0.3280|± |0.0210|
| | |none | 0|acc_norm |↑ | 0.4360|± |0.0222|
|piqa | 1|none | 0|acc |↑ | 0.7982|± |0.0094|
| | |none | 0|acc_norm |↑ | 0.8074|± |0.0092|
|truthfulqa |N/A |none | 0|acc |↑ | 0.4746|± |0.0116|
| | |none | 0|bleu_acc |↑ | 0.4700|± |0.0175|
| | |none | 0|bleu_diff |↑ | 0.3214|± |0.6045|
| | |none | 0|bleu_max |↑ |22.5895|± |0.7122|
| | |none | 0|rouge1_acc |↑ | 0.4798|± |0.0175|
| | |none | 0|rouge1_diff |↑ | 0.0846|± |0.7161|
| | |none | 0|rouge1_max |↑ |48.7180|± |0.7833|
| | |none | 0|rouge2_acc |↑ | 0.4149|± |0.0172|
| | |none | 0|rouge2_diff |↑ |-0.4656|± |0.8375|
| | |none | 0|rouge2_max |↑ |34.0585|± |0.8974|
| | |none | 0|rougeL_acc |↑ | 0.4651|± |0.0175|
| | |none | 0|rougeL_diff |↑ |-0.2804|± |0.7217|
| | |none | 0|rougeL_max |↑ |45.2232|± |0.7971|
| - truthfulqa_gen | 3|none | 0|bleu_acc |↑ | 0.4700|± |0.0175|
| | |none | 0|bleu_diff |↑ | 0.3214|± |0.6045|
| | |none | 0|bleu_max |↑ |22.5895|± |0.7122|
| | |none | 0|rouge1_acc |↑ | 0.4798|± |0.0175|
| | |none | 0|rouge1_diff |↑ | 0.0846|± |0.7161|
| | |none | 0|rouge1_max |↑ |48.7180|± |0.7833|
| | |none | 0|rouge2_acc |↑ | 0.4149|± |0.0172|
| | |none | 0|rouge2_diff |↑ |-0.4656|± |0.8375|
| | |none | 0|rouge2_max |↑ |34.0585|± |0.8974|
| | |none | 0|rougeL_acc |↑ | 0.4651|± |0.0175|
| | |none | 0|rougeL_diff |↑ |-0.2804|± |0.7217|
| | |none | 0|rougeL_max |↑ |45.2232|± |0.7971|
| - truthfulqa_mc1 | 2|none | 0|acc |↑ | 0.3905|± |0.0171|
| - truthfulqa_mc2 | 2|none | 0|acc |↑ | 0.5587|± |0.0156|
|winogrande | 1|none | 0|acc |↑ | 0.7388|± |0.0123|
| Groups |Version|Filter|n-shot| Metric | | Value | |Stderr|
|------------------|-------|------|-----:|-----------|---|------:|---|-----:|
|agieval |N/A |none | 0|acc |↑ | 0.5381|± |0.0049|
| | |none | 0|acc_norm |↑ | 0.5715|± |0.0056|
|mmlu |N/A |none | 0|acc |↑ | 0.6942|± |0.0037|
| - humanities |N/A |none | 0|acc |↑ | 0.6323|± |0.0067|
| - other |N/A |none | 0|acc |↑ | 0.7287|± |0.0077|
| - social_sciences|N/A |none | 0|acc |↑ | 0.7910|± |0.0072|
| - stem |N/A |none | 0|acc |↑ | 0.6581|± |0.0081|
|truthfulqa |N/A |none | 0|acc |↑ | 0.4746|± |0.0116|
| | |none | 0|bleu_acc |↑ | 0.4700|± |0.0175|
| | |none | 0|bleu_diff |↑ | 0.3214|± |0.6045|
| | |none | 0|bleu_max |↑ |22.5895|± |0.7122|
| | |none | 0|rouge1_acc |↑ | 0.4798|± |0.0175|
| | |none | 0|rouge1_diff|↑ | 0.0846|± |0.7161|
| | |none | 0|rouge1_max |↑ |48.7180|± |0.7833|
| | |none | 0|rouge2_acc |↑ | 0.4149|± |0.0172|
| | |none | 0|rouge2_diff|↑ |-0.4656|± |0.8375|
| | |none | 0|rouge2_max |↑ |34.0585|± |0.8974|
| | |none | 0|rougeL_acc |↑ | 0.4651|± |0.0175|
| | |none | 0|rougeL_diff|↑ |-0.2804|± |0.7217|
| | |none | 0|rougeL_max |↑ |45.2232|± |0.7971| |