---
library_name: transformers
base_model: Emm9625/Llama-3.2-1B-chatml
tags:
- axolotl
- generated_from_trainer
datasets:
- Emm9625/textwork-00-dedupe-0.75
- Emm9625/textwork-00-dedupe-0.75
- Emm9625/textwork-00-dedupe-0.75
model-index:
- name: tw-1b-dedupe-0.75-overfit
results: []
---
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<details><summary>See axolotl config</summary>
axolotl version: `0.6.0`
```yaml
# Original base model config
# base_model: Dans-DiscountModels/Meta-Llama-3.2-3B-ChatML
# Using smaller model instead
base_model: Emm9625/Llama-3.2-1B-chatml
# Original tokenizer config
# tokenizer_config: Dans-DiscountModels/Meta-Llama-3.2-3B-ChatML
# Using matching tokenizer for smaller model
tokenizer_config: Emm9625/Llama-3.2-1B-chatml
# Model loading configuration
load_in_8bit: false
load_in_4bit: false
strict: false
# Chat template configuration
chat_template: chatml
# Dataset configuration
datasets:
- path: Emm9625/textwork-00-dedupe-0.75
name: smol-constraints
split: train
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
train_on_eos: turn
# shards: 2
# shard_idx: 0
- path: Emm9625/textwork-00-dedupe-0.75
name: smol-rewrite
split: train
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
train_on_eos: turn
# shards: 2
# shard_idx: 0
- path: Emm9625/textwork-00-dedupe-0.75
name: smol-summarize
split: train
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
train_on_eos: turn
# shards: 2
# shard_idx: 0
test_datasets:
- path: Emm9625/textwork-00-dedupe-0.75
name: smol-constraints
split: test
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
train_on_eos: turn
shards: 5
shard_idx: 0
- path: Emm9625/textwork-00-dedupe-0.75
name: smol-rewrite
split: test
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
train_on_eos: turn
shards: 5
shard_idx: 0
- path: Emm9625/textwork-00-dedupe-0.75
name: smol-summarize
split: test
type: chat_template
field_messages: messages
message_field_role: role
message_field_content: content
train_on_eos: turn
shards: 5
shard_idx: 0
dataset_prepared_path: /notebooks/last_run_prepared
output_dir: /tmp/meow/
hub_model_id: Emm9625/tw-1b-dedupe-0.75-overfit
hub_strategy: checkpoint
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: true
# Model configuration
sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
# Unsloth optimizations
unsloth_cross_entropy_loss: true
unsloth_rms_norm: true
unsloth_rope: true
#Lora Optimizations
# unsloth_lora_mlp: true
# unsloth_lora_qkv: true
# unsloth_lora_o: true
# Training configuration
gradient_accumulation_steps: 1
micro_batch_size: 8
num_epochs: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
torch_compile: auto
train_on_inputs: false
group_by_length: false
bf16: true
gradient_checkpointing: true
flash_attention: true
# Training monitoring
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_ratio: 0.10
weight_decay: 0.00
saves_per_epoch: 1
evals_per_epoch: 5
save_safetensors: true
wandb_project: textwork-00-dedupe
logging_steps: 1
# Special tokens configuration
special_tokens:
eos_token: "<|im_end|>"
bos_token: "<|im_start|>"
fsdp:
fsdp_config:
```
</details><br>
# tw-1b-dedupe-0.75-overfit
This model is a fine-tuned version of [Emm9625/Llama-3.2-1B-chatml](https://huggingface.co/Emm9625/Llama-3.2-1B-chatml) on the Emm9625/textwork-00-dedupe-0.75, the Emm9625/textwork-00-dedupe-0.75 and the Emm9625/textwork-00-dedupe-0.75 datasets.
It achieves the following results on the evaluation set:
- Loss: 0.7782
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 71
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3266 | 0.0014 | 1 | 1.3293 |
| 0.8621 | 0.2006 | 144 | 0.8430 |
| 0.783 | 0.4011 | 288 | 0.8020 |
| 0.7862 | 0.6017 | 432 | 0.7834 |
| 0.7868 | 0.8022 | 576 | 0.7782 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0