File size: 3,915 Bytes
cf85519 2e3ee73 cf85519 9d5a867 cf85519 65ef3fa 8d689d6 81530b7 73b2f79 6a076d6 cf85519 73b2f79 6a076d6 cf85519 5706903 cf85519 6a076d6 cf85519 539e169 cf85519 539e169 cf85519 539e169 cf85519 539e169 cf85519 2e3ee73 |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: leveldevai/TurdusBeagle-7B
model-index:
- name: Metabird-7B
results: []
---
<!-- 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. -->
# Metabird-7B
<details><summary>See axolotl config</summary>
axolotl version: `0.3.0`
```yaml
base_model: leveldevai/TurdusBeagle-7B
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: shuyuej/metamath_gsm8k
type:
system_prompt: ""
field_instruction: question
field_output: answer
format: "[INST] {instruction} [/INST]"
no_input_format: "[INST] {instruction} [/INST]"
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
## Metabird
[<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)
This model is a fine-tuned version of [leveldevai/TurdusBeagle-7B](https://huggingface.co/leveldevai/TurdusBeagle-7B) on the shuyuej/metamath_gsm8k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4017
## Model description
More information soon
## Intended uses & limitations
More information soon
## Training and evaluation data
More information soon
## Training procedure
More information soon
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9074 | 0.05 | 1 | 0.9932 |
| 0.5012 | 0.26 | 5 | 0.4849 |
| 0.4204 | 0.53 | 10 | 0.4435 |
| 0.3748 | 0.79 | 15 | 0.4017 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
# [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_ConvexAI__Metabird-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |71.03|
|AI2 Reasoning Challenge (25-Shot)|69.54|
|HellaSwag (10-Shot) |87.54|
|MMLU (5-Shot) |65.27|
|TruthfulQA (0-shot) |57.94|
|Winogrande (5-shot) |83.03|
|GSM8k (5-shot) |62.85|
|