MLX Format and Quantizations for Magnum v3 9b CustomGemma2

Quantized to 8bpw and tested using the mlx_lm utility on a 64GiB URAM M1 Max.

See original model for further details.

Original model card

This is the 10th in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.

This model is fine-tuned on top of google/gemma-2-9b.

Prompting

Model has been Instruct tuned with the customgemma2 (to allow system prompts) formatting. A typical input would look like this:

"""<start_of_turn>system
system prompt<end_of_turn>
<start_of_turn>user
Hi there!<end_of_turn>
<start_of_turn>model
Nice to meet you!<end_of_turn>
<start_of_turn>user
Can I ask a question?<end_of_turn>
<start_of_turn>model
"""

SillyTavern templates

Below are Instruct and Context templates for use within SillyTavern.

context template

{
    "story_string": "<start_of_turn>system\n{{#if system}}{{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}{{trim}}<end_of_turn>\n",
    "example_separator": "",
    "chat_start": "",
    "use_stop_strings": false,
    "allow_jailbreak": false,
    "always_force_name2": true,
    "trim_sentences": false,
    "include_newline": false,
    "single_line": false,
    "name": "Magnum Gemma"
}

instruct template

{
    "system_prompt": "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.",
    "input_sequence": "<start_of_turn>user\n",
    "output_sequence": "<start_of_turn>assistant\n",
    "last_output_sequence": "",
    "system_sequence": "<start_of_turn>system\n",
    "stop_sequence": "<end_of_turn>",
    "wrap": false,
    "macro": true,
    "names": true,
    "names_force_groups": true,
    "activation_regex": "",
    "system_sequence_prefix": "",
    "system_sequence_suffix": "",
    "first_output_sequence": "",
    "skip_examples": false,
    "output_suffix": "<end_of_turn>\n",
    "input_suffix": "<end_of_turn>\n",
    "system_suffix": "<end_of_turn>\n",
    "user_alignment_message": "",
    "system_same_as_user": false,
    "last_system_sequence": "",
    "name": "Magnum Gemma"
}

Axolotl config

See axolotl config

base_model: google/gemma-2-9b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

#trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: anthracite-org/stheno-filtered-v1.1
    type: customgemma2
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    type: customgemma2
  - path: anthracite-org/nopm_claude_writing_fixed
    type: customgemma2
  - path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
    type: customgemma2
  - path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
    type: customgemma2
shuffle_merged_datasets: true
default_system_message: "You are an assistant that responds to the user."
dataset_prepared_path: magnum-v3-9b-data-customgemma2
val_set_size: 0.0
output_dir: ./magnum-v3-9b-customgemma2

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len:

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: magnum-9b
wandb_entity:
wandb_watch:
wandb_name: attempt-03-customgemma2
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.000006

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: false
eager_attention: true

warmup_steps: 50
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:

Credits

We'd like to thank Recursal / Featherless for sponsoring the training compute required for this model. Featherless has been hosting Magnum since the original 72b and has given thousands of people access to our releases.

We would also like to thank all members of Anthracite who made this finetune possible.

Training

The training was done for 2 epochs. We used 8xH100s GPUs graciously provided by Recursal AI / Featherless AI for the full-parameter fine-tuning of the model.

Safety

...

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	19.02
IFEval (0-Shot)	12.73
BBH (3-Shot)	34.12
MATH Lvl 5 (4-Shot)	6.12
GPQA (0-shot)	10.51
MuSR (0-shot)	15.06
MMLU-PRO (5-shot)	35.61

luigi86
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magnum-v3-9b-customgemma2_mlx-8bpw