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llama-3.2-1b-custom
jeanmcm/llama-3.2-1b-custom
llama-3.2-1b-custom is a machine learning model from jeanmcm. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as llama3.2.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- llama3.2
- Model type
- llama
- Access
- Public
- Created
- Jul 21, 2025
- Updated
- Jul 21, 2025
- SHA
- b0f3ebb5
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.8.0.dev0
base_model: NousResearch/Llama-3.2-1B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
datasets:
- path: teknium/GPT4-LLM-Cleaned
type: alpaca
val_set_size: 0.1
output_dir: ./outputs/lora-out
adapter: lora
lora_model_dir:
sequence_len: 2048
sample_packing: true
eval_sample_packing: true
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: "<|end_of_text|>"
# save_first_step: true # uncomment this to validate checkpoint saving works with your config
outputs/lora-out
This model is a fine-tuned version of NousResearch/Llama-3.2-1B on the teknium/GPT4-LLM-Cleaned dataset. It achieves the following results on the evaluation set:
- Loss: 1.0984
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: 2
- total_train_batch_size: 4
- 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: 10
- num_epochs: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3135 | 0.0009 | 1 | 1.3966 |
| 1.2185 | 0.2501 | 271 | 1.1416 |
| 1.3574 | 0.5002 | 542 | 1.1139 |
| 1.1916 | 0.7503 | 813 | 1.0984 |
Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Library
- peft
- Type
- llama
- License
- llama3.2
- Created
- Jul 21, 2025
- Updated
- Jul 21, 2025