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Name: L3.3-70B-Magnum-v4-SE-FP8-Dynamic
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Doctor-Shotgun/L3.3-70B-Magnum-v4-SE

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L3.3-70B-Magnum-v4-SE

The Magnum v4 series is complete, but here's something a little extra I wanted to tack on as I wasn't entirely satisfied with the results of v4 72B. "SE" for Special Edition - this model is finetuned from meta-llama/Llama-3.3-70B-Instruct as an rsLoRA adapter. The dataset is a slightly revised variant of the v4 data with some elements of the v2 data re-introduced.

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.

Here's the rsLoRA adapter for those merge-makers out there to play with.

Quantized models

Thank you to bartowski for the imatrix GGUF quants and mradermacher for the static GGUF quants.

Thank you to alpindale for the fp8 dynamic quant.

Exl2 quants courtesy of MikeRoz, including measurement.json if you need to make one with a different bitrate:

Usage

This model follows the Llama 3 prompt format. 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|>

{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.

SillyTavern JSON

json

{
    "instruct": {
        "wrap": false,
        "system_sequence": "<|start_header_id|>system<|end_header_id|>\n\n",
        "input_sequence": "<|start_header_id|>user<|end_header_id|>\n\n",
        "output_sequence": "<|start_header_id|>assistant<|end_header_id|>\n\n",
        "stop_sequence": "<|eot_id|>",
        "macro": true,
        "last_output_sequence": "",
        "activation_regex": "",
        "system_sequence_prefix": "",
        "system_sequence_suffix": "",
        "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",
        "skip_examples": false,
        "output_suffix": "<|eot_id|>",
        "input_suffix": "<|eot_id|>",
        "system_suffix": "<|eot_id|>",
        "user_alignment_message": "",
        "last_system_sequence": "",
        "system_same_as_user": false,
        "first_input_sequence": "",
        "last_input_sequence": "",
        "names_behavior": "always",
        "names_force_groups": true,
        "name": "Magnum SE L3 Instruct"
    },
    "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": true,
        "trim_sentences": false,
        "single_line": false,
        "name": "Magnum SE L3 Instruct"
    },
    "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 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

WandB

See axolotl config

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: LlamaForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
hub_model_id: Doctor-Shotgun/magnum-v4-se-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: anthracite-org/c2_logs_32k_llama3_qwen2_v1.3
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: anthracite-core/Gryphe-Opus-Charcard-Roleplay
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: anthracite-org/nopm_claude_writing_fixed
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: anthracite-org/kalo_opus_misc_240827
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: anthracite-org/kalo_misc_part2
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
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: 1
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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
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

Infermatic/L3.3-70B-Magnum-v4-SE-FP8-Dynamic