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--- |
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library_name: transformers |
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license: llama3.2 |
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license_link: https://huggingface.co/meta-llama/Llama-3.2-3B/blob/main/LICENSE.txt |
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base_model: meta-llama/Llama-3.2-3B |
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datasets: |
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- macadeliccc/US-SupremeCourtVerdicts |
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- macadeliccc/US-FederalLaws |
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tags: |
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- generated_from_trainer |
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- llama-3 |
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- spectrum |
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- axolotl |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# Magistrate 3.2 3B |
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Continued pretraining applied to [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) using no synthetic legal data. ~250M tokens. |
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The model achieves the following results on the evaluation set: |
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- Loss: 0.6802 |
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Instruct version is available [here]() |
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[<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) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.1` |
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```yaml |
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base_model: meta-llama/Llama-3.2-3B |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: json |
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data_files: "data/amendments_with_content_converted.json" |
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type: completion |
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- path: json |
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data_files: "data/federal_rules_converted.json" |
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type: completion |
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- path: json |
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data_files: "data/cornell_legal_encyclopedias_converted.json" |
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type: completion |
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- path: json |
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data_files: "data/pocket_guide_for_judges_converted.json" |
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type: completion |
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- path: json |
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data_files: "data/us_federal_code.json" |
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type: completion |
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- path: json |
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data_files: "data/us_supreme_court_summaries_converted.json" |
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type: completion |
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- path: json |
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data_files: "data/us_supreme_court_converted.json" |
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type: completion |
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- path: json |
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data_files: "data/ucfr.json" |
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type: completion |
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- path: json |
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data_files: "data/map-code-filtered.json" |
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type: completion |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./outputs/lora-out |
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sequence_len: 8192 |
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sample_packing: true |
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eval_sample_packing: false |
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pad_to_sequence_len: true |
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# adapter: lora |
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# lora_model_dir: |
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# lora_r: 128 |
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# lora_alpha: 32 |
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# lora_dropout: 0.05 |
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# lora_target_linear: true |
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# lora_fan_in_fan_out: |
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# lora_modules_to_save: |
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# - embed_tokens |
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# - lm_head |
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unfrozen_parameters: |
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- ^lm_head.weight$ |
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- ^model.embed_tokens.weight$ |
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# mlp.down_proj layers |
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- model.layers.0.mlp.down_proj |
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- model.layers.1.mlp.down_proj |
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- model.layers.17.mlp.down_proj |
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- model.layers.19.mlp.down_proj |
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- model.layers.18.mlp.down_proj |
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- model.layers.5.mlp.down_proj |
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- model.layers.20.mlp.down_proj |
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- model.layers.2.mlp.down_proj |
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- model.layers.4.mlp.down_proj |
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- model.layers.6.mlp.down_proj |
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- model.layers.3.mlp.down_proj |
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- model.layers.16.mlp.down_proj |
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- model.layers.15.mlp.down_proj |
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- model.layers.13.mlp.down_proj |
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# mlp.gate_proj layers |
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- model.layers.0.mlp.gate_proj |
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- model.layers.1.mlp.gate_proj |
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- model.layers.2.mlp.gate_proj |
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- model.layers.3.mlp.gate_proj |
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- model.layers.22.mlp.gate_proj |
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- model.layers.21.mlp.gate_proj |
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- model.layers.20.mlp.gate_proj |
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- model.layers.23.mlp.gate_proj |
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- model.layers.19.mlp.gate_proj |
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- model.layers.4.mlp.gate_proj |
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- model.layers.18.mlp.gate_proj |
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- model.layers.17.mlp.gate_proj |
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- model.layers.5.mlp.gate_proj |
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- model.layers.24.mlp.gate_proj |
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# mlp.up_proj layers |
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- model.layers.4.mlp.up_proj |
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- model.layers.3.mlp.up_proj |
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- model.layers.5.mlp.up_proj |
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- model.layers.6.mlp.up_proj |
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- model.layers.7.mlp.up_proj |
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- model.layers.2.mlp.up_proj |
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- model.layers.8.mlp.up_proj |
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- model.layers.14.mlp.up_proj |
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- model.layers.13.mlp.up_proj |
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- model.layers.11.mlp.up_proj |
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- model.layers.9.mlp.up_proj |
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- model.layers.1.mlp.up_proj |
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- model.layers.15.mlp.up_proj |
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- model.layers.12.mlp.up_proj |
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# self_attn.k_proj layers |
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- model.layers.25.self_attn.k_proj |
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- model.layers.22.self_attn.k_proj |
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- model.layers.19.self_attn.k_proj |
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- model.layers.20.self_attn.k_proj |
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- model.layers.17.self_attn.k_proj |
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- model.layers.24.self_attn.k_proj |
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- model.layers.23.self_attn.k_proj |
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- model.layers.18.self_attn.k_proj |
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- model.layers.21.self_attn.k_proj |
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- model.layers.27.self_attn.k_proj |
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- model.layers.15.self_attn.k_proj |
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- model.layers.10.self_attn.k_proj |
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- model.layers.6.self_attn.k_proj |
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- model.layers.5.self_attn.k_proj |
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# self_attn.o_proj layers |
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wandb_project: |
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wandb_entity: |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 3 |
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optimizer: paged_adamw_32bit |
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# Gradient clipping max norm |
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max_grad_norm: 1.0 |
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noisy_embedding_alpha: 0 # no noisy embedding to ensure maximal memorization |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: auto |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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s2_attention: |
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warmup_steps: 690 |
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evals_per_epoch: 2 |
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eval_table_size: |
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eval_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: deepspeed_configs/zero3.json |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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## Model description |
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This is a base model trained on US Supreme Court proceedings, US federal code and regulations. |
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## Intended uses & limitations |
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This model is intended for research purposes. You are liable for all model outputs. |
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## Training and evaluation data |
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The training data consists of US Supreme Court verdicts, federal regulations, laws and treaties. |
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Some other resources have been included from institutions like CLL to fill in the gaps in knowledge for industry jargon. |
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## Training procedure |
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Spectrum top 35% fine tune. Thanks to the cognitive computations team for the work done on spectrum. |
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Methodology based on Cohere's paper: [To Code, or Not To Code? Exploring Impact of Code in Pre-training](https://arxiv.org/abs/2408.10914) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 690 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.3589 | 0.0004 | 1 | 1.5640 | |
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| 0.9936 | 0.4984 | 1154 | 0.9440 | |
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| 0.8384 | 0.9968 | 2308 | 0.8392 | |
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| 0.8226 | 1.4963 | 3462 | 0.7802 | |
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| 0.6568 | 1.9949 | 4616 | 0.7059 | |
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| 0.5163 | 2.4923 | 5770 | 0.6886 | |
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| 0.492 | 2.9922 | 6924 | 0.6802 | |
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### Framework versions |
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- Transformers 4.45.0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.0 |
