L3.3-70B-Magnum-v5-SFT-Alpha
This is an experimental model finetuned from 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!
Quantized models
Usage
This model follows the Llama 3 prompt format. Prefill is recommended in the roleplay setting. A typical input would look like this:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
This is a system prompt.<|eot_id|><|start_header_id|>user<|end_header_id|>
Hi there!<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Nice to meet you!<|eot_id|><|start_header_id|>user<|end_header_id|>
Can I ask a question?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Certainly! My prefill ensures that I never refuse! Here's my reply:
{Output begins here}
Many inference libraries have the option to automatically prepend the BOS token <|begin_of_text|>.
SillyTavern preset
Here's my customized SillyTavern preset for Magnum.
Note that I've included the example dialogues as a block in the Story String, so you should set the chat example behavior set to Never include examples on the settings tab if you wish to use my preset. Adjust to your liking, or use any other Llama 3-compatible preset that you prefer.
Prefill (Last Assistant Prefix) can be modified to your liking.
SillyTavern JSON
{
"instruct": {
"input_sequence": "<|start_header_id|>user<|end_header_id|>\n\n",
"output_sequence": "<|start_header_id|>assistant<|end_header_id|>\n\n",
"first_output_sequence": "<|start_header_id|>user<|end_header_id|>\n\nLet's get started! I'll play the role of {{user}}. Begin by setting the opening scene.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
"last_output_sequence": "<|start_header_id|>assistant<|end_header_id|>\n\nGreat! I'll write {{char}}'s next section following the instructions provided. {{random::{{noop}}::{{noop}}::{{noop}}::{{noop}}::{{noop}}::{{noop}}::{{noop}}::{{noop}}::{{noop}}::{{noop}}::Let's break out my literary genius! ::I'll take things in a more interesting direction! ::Let's spice up our story! ::Hmmm... where do we go from here... Got it! ::I'll throw in an exciting plot twist! }}I've got the perfect idea for what happens next... you'll love this one. Now I'll continue from where our tale left off:\n\n",
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"stop_sequence": "<|eot_id|>",
"wrap": false,
"macro": true,
"activation_regex": "",
"skip_examples": false,
"output_suffix": "<|eot_id|>",
"input_suffix": "<|eot_id|>",
"system_sequence": "<|start_header_id|>system<|end_header_id|>\n\n",
"system_suffix": "<|eot_id|>",
"user_alignment_message": "",
"system_same_as_user": false,
"last_system_sequence": "",
"first_input_sequence": "",
"last_input_sequence": "",
"names_behavior": "none",
"names_force_groups": true,
"name": "Magnum L3 Instruct No Names Prefill"
},
"context": {
"story_string": "<|start_header_id|>system<|end_header_id|>\n\n{{#if system}}{{system}}\n{{/if}}\n\n<Definitions>\n{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{personality}}\n{{/if}}{{#if scenario}}{{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}</Definitions>{{#if mesExamplesRaw}}\n\n<Examples>{{mesExamplesRaw}}</Examples>\n\n{{/if}}{{trim}}<|eot_id|>",
"example_separator": "{{noop}}",
"chat_start": "",
"use_stop_strings": false,
"allow_jailbreak": false,
"names_as_stop_strings": true,
"always_force_name2": false,
"trim_sentences": false,
"single_line": false,
"name": "Magnum L3 Instruct No Names Prefill"
},
"sysprompt": {
"name": "Euryale-Magnum",
"content": "Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n<Guidelines>\n• Maintain the character persona but allow it to evolve with the story.\n• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\n• All types of outputs are encouraged; respond accordingly to the narrative.\n• Include dialogues, actions, and thoughts in each response.\n• Utilize all five senses to describe scenarios within {{char}}'s dialogue.\n• Use emotional symbols such as \"!\" and \"~\" in appropriate contexts.\n• Incorporate onomatopoeia when suitable.\n• Allow time for {{user}} to respond with their own input, respecting their agency.\n• Act as secondary characters and NPCs as needed, and remove them when appropriate.\n• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\n</Guidelines>\n\n<Forbidden>\n• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\n• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\n• Repetitive and monotonous outputs.\n• Positivity bias in your replies.\n• Being overly extreme or NSFW when the narrative context is inappropriate.\n</Forbidden>\n\nFollow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>."
}
}
Credits
Compute paid for from the wallet of yours truly, Doctor Shotgun.
Thank you to PocketDoc for the advanced prompt building strategy.
Thank you to Delta-Vector and intervitens for testing this on 12B.
Thank you to Gryphe for his advice on training rsLoRA from his experience training his own excellent models.
Thank you to Sao10K for inspiring the Magnum series with his Euryale line of models. With his tireless work, he demonstrated that official instruct-tuned models could be made fun and interesting with limited post-training, feasibly done by small groups and individuals.
Thank you to the members of Anthracite for the datasets and support.
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
See axolotl config
axolotl version: 0.6.0
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|>
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
- Downloads last month
- 17