--- License: apache-2.0 Language: - En Pipeline_tag: text-generation Base_model: 01-ai/Yi-1.5-34B-32K Tags: - Chat --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/9yEmnTDG9bcC_bxwuDU6G.png) # magnum-v3-34 - EXL2 4.6bpw rpcal mk2 This is a 4.6bpw EXL2 quant of [anthracite-org/magnum-v3-34b](https://huggingface.co/anthracite-org/magnum-v3-34b) This quant was made using exllamav2-0.1.9 with [Bluemoon-light dataset](https://huggingface.co/datasets/ParasiticRogue/Bluemoon-Light) for RP. I tested this quant shortly in some random RPs (including 8k+ RPs where remembering and understanding specific facts in the context is needed) and it seems to work fine. ## Prompt Templates Uses ChatML format like mentioned below. ### Original readme below --- This is the 9th 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 [Yi-1.5-34 B-32 K](https://huggingface.co/01-ai/Yi-1.5-34B-32K). ## Prompting Model has been Instruct tuned with the ChatML formatting. A typical input would look like this: ```py """<|im_start|>system system prompt<|im_end|> <|im_start|>user Hi there!<|im_end|> <|im_start|>assistant Nice to meet you!<|im_end|> <|im_start|>user Can I ask a question?<|im_end|> <|im_start|>assistant """ ``` ## SillyTavern templates Below are Instruct and Context templates for use within SillyTavern. In our testing a min_p of 0.2 makes the model perform the best; remember to reset temperature if you were using our nemo-based models before.
context template ```yaml { "story_string": "<|im_start|>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}}<|im_end|>\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 ChatML" } ```

instruct template ```yaml { "system_prompt": "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.", "input_sequence": "<|im_start|>user\n", "output_sequence": "<|im_start|>assistant\n", "last_output_sequence": "", "system_sequence": "<|im_start|>system\n", "stop_sequence": "<|im_end|>", "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": "<|im_end|>\n", "input_suffix": "<|im_end|>\n", "system_suffix": "<|im_end|>\n", "user_alignment_message": "", "system_same_as_user": false, "last_system_sequence": "", "name": "Magnum ChatML" } ```

## Axolotl config
See axolotl config ```yaml base_model: 01-ai/Yi-1.5-34B-32K 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: sharegpt conversation: chatml - path: anthracite-org/kalo-opus-instruct-22k-no-refusal type: sharegpt conversation: chatml - path: anthracite-org/nopm_claude_writing_fixed type: sharegpt conversation: chatml - path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml - path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml chat_template: chatml shuffle_merged_datasets: true default_system_message: "You are an assistant that responds to the user." dataset_prepared_path: magnum-v2-34b-1.5-data val_set_size: 0.0 output_dir: ./magnum-v2-34b-32k-r1 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-v2-34b-1.5-32k wandb_entity: wandb_watch: wandb_name: attempt-01 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: 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 compute for this train, Featherless has been hosting our Magnum models since the first 72 B and has given thousands of people access to our models and helped us grow. 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) - [lodrick-the-lafted/NopmWritingStruct](https://huggingface.co/datasets/lodrick-the-lafted/NopmWritingStruct) - [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 ...