---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: pints-ai/1.5-Pints-16K-v0.1
model-index:
- name: tangledgroup/tangled-llama-pints-1.5b-v0.2-instruct
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: pints-ai/1.5-Pints-16K-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: tangledgroup/tangled-llama-pints-1.5b-v0.2-dataset
    type: sharegpt
    conversation: chatml
chat_template: chatml
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
# optimizer: adamw_torch_fused
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

loss_watchdog_threshold: 15.0
loss_watchdog_patience: 3

warmup_steps: 10
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
```

</details><br>

# outputs/qlora-out

This model is a fine-tuned version of [pints-ai/1.5-Pints-16K-v0.1](https://huggingface.co/pints-ai/1.5-Pints-16K-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9847

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.1396        | 0.0011 | 1    | 1.1313          |
| 1.0777        | 0.3332 | 295  | 1.0278          |
| 1.0219        | 0.6665 | 590  | 1.0119          |
| 1.0006        | 0.9997 | 885  | 1.0020          |
| 1.0385        | 1.3307 | 1180 | 0.9954          |
| 0.9405        | 1.6639 | 1475 | 0.9902          |
| 0.9249        | 1.9972 | 1770 | 0.9867          |
| 0.9951        | 2.3282 | 2065 | 0.9856          |
| 0.9713        | 2.6616 | 2360 | 0.9848          |
| 0.9576        | 2.9949 | 2655 | 0.9847          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.45.0.dev0
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.19.1
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_tangledgroup__tangled-llama-pints-1.5b-v0.2-instruct)

|      Metric       |Value|
|-------------------|----:|
|Avg.               | 4.66|
|IFEval (0-Shot)    |17.24|
|BBH (3-Shot)       | 4.08|
|MATH Lvl 5 (4-Shot)| 0.76|
|GPQA (0-shot)      | 0.00|
|MuSR (0-shot)      | 4.57|
|MMLU-PRO (5-shot)  | 1.30|