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Ritual-Net
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answer-emojis

PEFT
Safetensors
llama
axolotl
Generated from Trainer
8-bit precision
bitsandbytes
Model card Files Community
answer-emojis
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---
base_model: NousResearch/Llama-2-7b-hf
library_name: peft
tags:
- axolotl
- generated_from_trainer
model-index:
- name: answer-emojis
  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. -->

[<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)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: NousResearch/Llama-2-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: formatted_math_ratio_02_emojianswers_10k.jsonl
    ds_type: json
    type: alpaca
val_set_size: 0.05
dataset_prepared_path: 
output_dir: ./outputs/ppml-formatted

hf_use_auth_token: True
hub_model_id: Ritual-Net/answer-emojis
hub_strategy: all_checkpoints

eval_sample_packing: False


sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: ppml
wandb_entity: ritualnah
wandb_watch: 
wandb_name: emojianswers
wandb_log_model: "checkpoint"

lora_modules_to_save:
  - embed_tokens
  - lm_head


gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128
saves_every_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
tokens: # these are delimiters
  - "[INST]"
  - "[/INST]"
```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/ritualnah/ppml/runs/3q9smy0v)
# answer-emojis

This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5239

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- 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: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0155        | 0.0082 | 1    | 1.2302          |
| 0.5161        | 0.5031 | 61   | 0.5744          |
| 0.5398        | 1.0062 | 122  | 0.5379          |
| 0.4614        | 1.4990 | 183  | 0.5295          |
| 0.4323        | 2.0021 | 244  | 0.5178          |
| 0.3823        | 2.4948 | 305  | 0.5239          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1