File size: 12,314 Bytes
db2b013 bff2cd7 9b4781a bff2cd7 9b4781a bff2cd7 9b4781a db2b013 bff2cd7 9b4781a 9792b12 6c2bc3b 9b4781a bff2cd7 9b4781a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 |
---
license: cc-by-nc-4.0
base_model: mlabonne/NeuralMonarch-7B
tags:
- generated_from_trainer
- axolotl
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: AlphaMonarch-laser
results: []
datasets:
- argilla/OpenHermes2.5-dpo-binarized-alpha
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AlphaMonarch-laser
AlphaMonarch-laser is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset but achieves better performance then [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B/) using LaserQLoRA. I have fine-tuned this model only on half of the projections, but have achieved better results as compared to the version released by Maximme Labonne. I have trained this model for 1080 steps.
AlphaMonarch-laser is ranking 1 on YALL - [Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).
## 🏆 Evaluation results
# Nous Benchmark
### AGIEVAL
| Task | Version | Metric | Value | StdErr |
|---------------------------------|---------|--------------|--------|--------|
| agieval_aqua_rat | 0 | acc | 28.35% | 2.83% |
| agieval_aqua_rat | 0 | acc_norm | 26.38% | 2.77% |
| agieval_logiqa_en | 0 | acc | 38.25% | 1.91% |
| agieval_logiqa_en | 0 | acc_norm | 38.10% | 1.90% |
| agieval_lsat_ar | 0 | acc | 23.91% | 2.82% |
| agieval_lsat_ar | 0 | acc_norm | 23.48% | 2.80% |
| agieval_lsat_lr | 0 | acc | 52.75% | 2.21% |
| agieval_lsat_lr | 0 | acc_norm | 53.92% | 2.21% |
| agieval_lsat_rc | 0 | acc | 66.91% | 2.87% |
| agieval_lsat_rc | 0 | acc_norm | 67.29% | 2.87% |
| agieval_sat_en | 0 | acc | 78.64% | 2.86% |
| agieval_sat_en | 0 | acc_norm | 78.64% | 2.86% |
| agieval_sat_en_without_passage | 0 | acc | 45.15% | 3.48% |
| agieval_sat_en_without_passage | 0 | acc_norm | 44.17% | 3.47% |
| agieval_sat_math | 0 | acc | 33.18% | 3.18% |
| agieval_sat_math | 0 | acc_norm | 31.36% | 3.14% |
Average: 28.41%
### GPT4ALL
| Task | Version | Metric | Value | StdErr |
|--------------|---------|----------|-------|--------|
| arc_challenge| 0 | acc | 66.30%| ± 1.38%|
| | | acc_norm | 68.26%| ± 1.36%|
| arc_easy | 0 | acc | 86.57%| ± 0.70%|
| | | acc_norm | 80.81%| ± 0.81%|
| boolq | 1 | acc | 87.16%| ± 0.59%|
| hellaswag | 0 | acc | 69.60%| ± 0.46%|
| | | acc_norm | 87.45%| ± 0.33%|
| openbookqa | 0 | acc | 39.20%| ± 2.19%|
| | | acc_norm | 49.60%| ± 2.24%|
| piqa | 0 | acc | 83.03%| ± 0.88%|
| | | acc_norm | 84.87%| ± 0.84%|
| winogrande | 0 | acc | 81.06%| ± 1.10%|
Average: 76.98%
### TRUTHFUL-QA
| Task | Version | Metric | Value | StdErr |
|---------------|---------|--------|-------|--------|
| truthfulqa_mc | 1 | mc1 | 63.04%| ± 1.69%|
| truthfulqa_mc | 1 | mc2 | 78.39%| ± 1.37%|
Average: 70.71%
### BIGBENCH
| Task | Version | Metric | Value | StdErr |
|------------------------------------------------|---------|-----------------------|-------|--------------------|
| bigbench_causal_judgement | 0 | multiple_choice_grade| 60.00%| ± 3.56% |
| bigbench_date_understanding | 0 | multiple_choice_grade| 62.06%| ± 2.53% |
| bigbench_disambiguation_qa | 0 | multiple_choice_grade| 54.26%| ± 3.11% |
| bigbench_geometric_shapes | 0 | multiple_choice_grade| 23.96%| ± 2.26% |
| | | exact_str_match | 0.00% | ± 0.00% |
| bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade| 32.80%| ± 2.10% |
| bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade| 23.86%| ± 1.61% |
| bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade| 59.33%| ± 2.84% |
| bigbench_movie_recommendation | 0 | multiple_choice_grade| 58.00%| ± 2.21% |
| bigbench_navigate | 0 | multiple_choice_grade| 56.00%| ± 1.57% |
| bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade| 69.20%| ± 1.03% |
| bigbench_ruin_names | 0 | multiple_choice_grade| 55.36%| ± 2.35% |
| bigbench_salient_translation_error_detection | 0 | multiple_choice_grade| 41.48%| ± 1.56% |
| bigbench_snarks | 0 | multiple_choice_grade| 73.48%| ± 3.29% |
| bigbench_sports_understanding | 0 | multiple_choice_grade| 76.06%| ± 1.36% |
| bigbench_temporal_sequences | 0 | multiple_choice_grade| 55.50%| ± 1.57% |
| bigbench_tracking_shuffled_objects_five_objects| 0 | multiple_choice_grade| 23.28%| ± 1.20% |
| bigbench_tracking_shuffled_objects_seven_objects| 0 | multiple_choice_grade| 19.37%| ± 0.94% |
| bigbench_tracking_shuffled_objects_three_objects| 0 | multiple_choice_grade| 59.33%| ± 2.84% |
Average: 55.37%
# Openllm Benchmark
| Task |Version| Metric |Value| |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge| 0|acc |70.12|± | 1.30|
| | |acc_norm|73.27|± | 1.29|
|hellaswag | 0|acc |71.80|± | 0.44|
| | |acc_norm|89.20|± | 0.30|
|gsm8k | 0|acc |66.77|± | 1.2 |
|winogrande | 0|acc |84.6 |± | 1.0 |
Average: 73.5%
### TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |62.79|± | 1.69|
| | |mc2 |77.90|± | 1.37|
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1080
### 📝 Axolotl Configuration
```yaml
base_model: mlabonne/NeuralMonarch-7B
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: true
strict: false
rl: dpo
chat_template: chatml
datasets:
- path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
split: train
type: chatml.intel
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out
adapter: qlora
lora_model_dir:
sequence_len: 1800
sample_packing: false
pad_to_sequence_len: false
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- layers.1.self_attn.q_proj
- layers.0.self_attn.q_proj
- layers.15.self_attn.q_proj
- layers.12.self_attn.q_proj
- layers.11.self_attn.q_proj
- layers.14.self_attn.q_proj
- layers.9.self_attn.q_proj
- layers.16.self_attn.q_proj
- layers.30.self_attn.q_proj
- layers.18.self_attn.q_proj
- layers.13.self_attn.q_proj
- layers.10.self_attn.q_proj
- layers.7.self_attn.q_proj
- layers.8.self_attn.q_proj
- layers.4.self_attn.q_proj
- layers.19.self_attn.q_proj
- layers.27.self_attn.k_proj
- layers.24.self_attn.k_proj
- layers.25.self_attn.k_proj
- layers.22.self_attn.k_proj
- layers.26.self_attn.k_proj
- layers.29.self_attn.k_proj
- layers.23.self_attn.k_proj
- layers.28.self_attn.k_proj
- layers.21.self_attn.k_proj
- layers.31.self_attn.k_proj
- layers.30.self_attn.k_proj
- layers.20.self_attn.k_proj
- layers.5.self_attn.k_proj
- layers.19.self_attn.k_proj
- layers.17.self_attn.k_proj
- layers.18.self_attn.k_proj
- layers.19.self_attn.v_proj
- layers.24.self_attn.v_proj
- layers.18.self_attn.v_proj
- layers.5.self_attn.v_proj
- layers.3.self_attn.v_proj
- layers.16.self_attn.v_proj
- layers.23.self_attn.v_proj
- layers.27.self_attn.v_proj
- layers.25.self_attn.v_proj
- layers.26.self_attn.v_proj
- layers.20.self_attn.v_proj
- layers.6.self_attn.v_proj
- layers.15.self_attn.v_proj
- layers.17.self_attn.v_proj
- layers.29.self_attn.v_proj
- layers.22.self_attn.v_proj
- layers.12.self_attn.o_proj
- layers.9.self_attn.o_proj
- layers.14.self_attn.o_proj
- layers.0.self_attn.o_proj
- layers.6.self_attn.o_proj
- layers.8.self_attn.o_proj
- layers.10.self_attn.o_proj
- layers.11.self_attn.o_proj
- layers.13.self_attn.o_proj
- layers.24.self_attn.o_proj
- layers.7.self_attn.o_proj
- layers.15.self_attn.o_proj
- layers.5.self_attn.o_proj
- layers.17.self_attn.o_proj
- layers.25.self_attn.o_proj
- layers.4.self_attn.o_proj
- layers.31.mlp.gate_proj
- layers.30.mlp.gate_proj
- layers.4.mlp.gate_proj
- layers.3.mlp.gate_proj
- layers.29.mlp.gate_proj
- layers.28.mlp.gate_proj
- layers.6.mlp.gate_proj
- layers.27.mlp.gate_proj
- layers.5.mlp.gate_proj
- layers.26.mlp.gate_proj
- layers.25.mlp.gate_proj
- layers.7.mlp.gate_proj
- layers.2.mlp.gate_proj
- layers.24.mlp.gate_proj
- layers.23.mlp.gate_proj
- layers.10.mlp.gate_proj
- layers.6.mlp.up_proj
- layers.4.mlp.up_proj
- layers.5.mlp.up_proj
- layers.27.mlp.up_proj
- layers.25.mlp.up_proj
- layers.26.mlp.up_proj
- layers.17.mlp.up_proj
- layers.24.mlp.up_proj
- layers.7.mlp.up_proj
- layers.10.mlp.up_proj
- layers.3.mlp.up_proj
- layers.11.mlp.up_proj
- layers.23.mlp.up_proj
- layers.9.mlp.up_proj
- layers.14.mlp.up_proj
- layers.18.mlp.up_proj
- layers.19.mlp.down_proj
- layers.20.mlp.down_proj
- layers.18.mlp.down_proj
- layers.21.mlp.down_proj
- layers.29.mlp.down_proj
- layers.1.mlp.down_proj
- layers.22.mlp.down_proj
- layers.28.mlp.down_proj
- layers.23.mlp.down_proj
- layers.30.mlp.down_proj
- layers.17.mlp.down_proj
- layers.4.mlp.down_proj
- layers.2.mlp.down_proj
- layers.15.mlp.down_proj
- layers.5.mlp.down_proj
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 5e-7
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 1
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 1080
max_steps: 1080
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.17.0
- Tokenizers 0.15.0
- axolotl: 0.4.0
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |