---
library_name: peft
license: llama3.3
base_model: meta-llama/Llama-3.3-70B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- AquaV/c1-sharegpt-advanced-prefills-filtered
- AquaV/c2-sharegpt-advanced-prefills-filtered
- AquaV/rainy-sharegpt-advanced-prefills-filtered
- anthracite-core/Gryphe-Opus-Charcard-Roleplay
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- lodrick-the-lafted/kalo-opus-instruct-3k-filtered
- anthracite-org/nopm_claude_writing_fixed
- anthracite-org/kalo_opus_misc_240827
- anthracite-org/kalo_misc_part2
- NewEden/Claude-Instruct-5K
- NewEden/Claude-Instruct-2.7K
model-index:
- name: magnum-v5-sft-prototype-70b-lora
  results: []
---

# Magnum-v5-70B-SFT-Alpha-LoRA

This is an experimental model finetuned from [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as an rsLoRA adapter.
The prototype v5 SFT dataset expands on the v4 dataset with additional data and a custom prompt strategy.

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.

This model performs best with a prefill and with all settings to prepend character names **disabled**, otherwise it can be a bit more finnicky to work with than L3.3-70B-Magnum-v4-SE.
There seems to be a very strong markdown/asterisk style bias when character names are prepended.
Feedback is appreciated!

[Merged full model](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v5-SFT-Alpha)

## 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/fbkauk0g?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: `0.6.0`
```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
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: AquaV/c1-sharegpt-advanced-prefills-filtered
    type: dan-chat-advanced-llama3
  - path: AquaV/c2-sharegpt-advanced-prefills-filtered
    type: dan-chat-advanced-llama3
  - path: AquaV/rainy-sharegpt-advanced-prefills-filtered
    type: dan-chat-advanced-llama3
  - path: anthracite-core/Gryphe-Opus-Charcard-Roleplay
    type: dan-chat-advanced-llama3
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    type: dan-chat-advanced-llama3
  - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
    type: dan-chat-advanced-llama3
  - path: anthracite-org/nopm_claude_writing_fixed
    type: dan-chat-advanced-llama3
  - path: anthracite-org/kalo_opus_misc_240827
    type: dan-chat-advanced-llama3
  - path: anthracite-org/kalo_misc_part2
    type: dan-chat-advanced-llama3
  - path: NewEden/Claude-Instruct-5K
    type: dan-chat-advanced-llama3
  - path: NewEden/Claude-Instruct-2.7K
    type: dan-chat-advanced-llama3
shuffle_merged_datasets: true
dataset_prepared_path: /home/docshotgun/data/magnum-70b-data
val_set_size: 0.0
output_dir: /home/docshotgun/data/70b-lora-out

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

sequence_len: 32768
sample_packing: true
eval_sample_packing: false
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: 4.0e-5
max_grad_norm: 3.0

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: unsloth
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.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 0.14.0
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0