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---
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|
|