Doctor-Shotgun
/

L3.3-70B-Magnum-Diamond-LoRA

@@ -1,202 +1,157 @@
 ---
-base_model: meta-llama/Llama-3.3-70B-Instruct
 library_name: peft
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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 ### Framework versions
-- PEFT 0.15.2

 ---
 library_name: peft
+license: llama3.3
+base_model: meta-llama/Llama-3.3-70B-Instruct
+tags:
+- axolotl
+- generated_from_trainer
 ---
+# L3.3-70B-Magnum-Diamond-LoRA
+Magnum "Diamond" in reference to the intense heat and pressure (generated through matrix multiplications) needed to turn the coal-esque material of dry, assistant-tuned models into creative writing gems!
+This model is finetuned from [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as an rsLoRA adapter. It uses the same data mix as [Doctor-Shotgun/L3.3-70B-Magnum-v5-SFT-Alpha](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v5-SFT-Alpha), however with pre-tokenization and modifications to the custom loss masking.
+It's for all intents and purposes a version update to the former model.
+This model should perform competently with or without prepending character names, and with or without prefill.
+The objective, as with the other Magnum models, is to emulate the prose style and quality of the Claude 3 Sonnet/Opus series of models on a local scale, so don't be surprised to see "Claude-isms" in its output.
+[Merged full model](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-Diamond)
+## Intended uses and limitations
+This model is intended for creative writing and roleplay purposes.
+It may show biases similar to those observed in contemporary LLM-based roleplay, in addition to those exhibited by the Claude 3 series of models and the base model.
+All outputs should be considered fiction, as this model is not intended to provide factual information or advice.
+## Training procedure
+[WandB](https://wandb.ai/doctorshotgun/70b-magnum-lora/runs/acnk2imq?nw=nwuserdoctorshotgun)
+[<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: `0.9.2`
+```yaml
+base_model: meta-llama/Llama-3.3-70B-Instruct
+base_model_ignore_patterns: "*/*"
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+hub_model_id: Doctor-Shotgun/magnum-v5-sft-prototype-70b-lora-rev1
+hub_strategy: "all_checkpoints"
+push_dataset_to_hub:
+hf_use_auth_token: true
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+datasets:
+  - path: anthracite-core/magnum-v5-sft-proto-llama3-rev1-32k
+    ds_type: parquet
+    type:
+shuffle_merged_datasets: true
+dataset_prepared_path: /workspace/magnum-70b-data
+val_set_size: 0.0
+output_dir: /workspace/70b-lora-out
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_layer_norm: true
+liger_glu_activation: true
+liger_fused_linear_cross_entropy: false
+cut_cross_entropy: true
+sequence_len: 32768
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+adapter: lora
+lora_model_dir:
+lora_r: 128
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+peft_use_rslora: true
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
+wandb_project: 70b-magnum-lora
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+gradient_accumulation_steps: 1
+micro_batch_size: 2
+num_epochs: 2
+optimizer: paged_ademamix_8bit
+lr_scheduler: cosine
+learning_rate: 4e-5
+max_grad_norm: 1.0
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+gradient_checkpointing: offload
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+s2_attention:
+warmup_steps: 40
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens:
+saves_per_epoch: 2
+debug:
+deepspeed: ./deepspeed_configs/zero3_bf16_torch_compile.json
+weight_decay: 0.01
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+```
+</details><br>
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 4e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- total_train_batch_size: 16
+- total_eval_batch_size: 16
+- optimizer: Use paged_ademamix_8bit and the args are:
+No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 40
+- num_epochs: 2.0
 ### Framework versions
+- PEFT 0.15.2
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.1
+- Tokenizers 0.21.1