--- license: gemma base_model: google/gemma-2-9b model-index: - name: magnum-v3-9b-customgemma2 results: [] --- ## remember to downgrade to exllama 0.1.8 or update to 0.2.0 to receive upstream fixes --- ## This repo contains EXL2 quants of the model. If you need the original weights, please find them [here](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2). ## Base repo only contains the measurement file, see revisions for your quant of choice. - [measurement.json](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2-exl2/tree/main) - [3.0bpw](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2-exl2/tree/3.0bpw) - [4.0bpw](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2-exl2/tree/4.0bpw) - [5.0bpw](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2-exl2/tree/5.0bpw) - [6.0bpw](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2-exl2/tree/6.0bpw) - [8.0bpw](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2-exl2/tree/8.0bpw) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/9ZBUlmzDCnNmQEdUUbyEL.png) 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](https://huggingface.co/google/gemma-2-9b). ## Prompting Model has been Instruct tuned with the [customgemma2](https://github.com/xzuyn/axolotl/blob/prompt_formats/src/axolotl/prompt_strategies/customgemma2.py) (to allow system prompts) formatting. A typical input would look like this: ```py """system system prompt user Hi there! model Nice to meet you! user Can I ask a question? model """ ``` ## SillyTavern templates Below are Instruct and Context templates for use within SillyTavern.
context template ```yaml { "story_string": "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}}\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 ```yaml { "system_prompt": "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.", "input_sequence": "user\n", "output_sequence": "assistant\n", "last_output_sequence": "", "system_sequence": "system\n", "stop_sequence": "", "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": "\n", "input_suffix": "\n", "system_suffix": "\n", "user_alignment_message": "", "system_same_as_user": false, "last_system_sequence": "", "name": "Magnum Gemma" } ```


## Axolotl config
See axolotl config ```yaml 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. - [anthracite-org/stheno-filtered-v1.1](https://huggingface.co/datasets/anthracite-org/stheno-filtered-v1.1) - [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal) - [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed) - [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned) - [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned) ## Training The training was done for 2 epochs. We used 8x[H100s](https://www.nvidia.com/en-us/data-center/h100/) GPUs graciously provided by [Recursal AI](https://recursal.ai/) / [Featherless AI](https://featherless.ai/) for the full-parameter fine-tuning of the model. [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) ## Safety ...