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README.md
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---
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License: apache-2.0
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Language:
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- En
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Pipeline_tag: text-generation
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Base_model: 01-ai/Yi-1.5-34B-32K
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Tags:
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- Chat
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/9yEmnTDG9bcC_bxwuDU6G.png)
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This is the 9th in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.
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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).
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## Prompting
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Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:
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```py
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"""<|im_start|>system
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system prompt<|im_end|>
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<|im_start|>user
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Hi there!<|im_end|>
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<|im_start|>assistant
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Nice to meet you!<|im_end|>
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<|im_start|>user
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Can I ask a question?<|im_end|>
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<|im_start|>assistant
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"""
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```
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## SillyTavern templates
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Below are Instruct and Context templates for use within SillyTavern.
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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.
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<details><summary>context template</summary>
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```yaml
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{
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"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",
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"example_separator": "",
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"chat_start": "",
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"use_stop_strings": false,
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"allow_jailbreak": false,
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"always_force_name2": true,
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"trim_sentences": false,
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"include_newline": false,
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"single_line": false,
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"name": "Magnum ChatML"
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}
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```
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</details><br>
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<details><summary>instruct template</summary>
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```yaml
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{
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"system_prompt": "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.",
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"input_sequence": "<|im_start|>user\n",
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"output_sequence": "<|im_start|>assistant\n",
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"last_output_sequence": "",
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"system_sequence": "<|im_start|>system\n",
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"stop_sequence": "<|im_end|>",
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"wrap": false,
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"macro": true,
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"names": true,
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"names_force_groups": true,
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"activation_regex": "",
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"system_sequence_prefix": "",
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"system_sequence_suffix": "",
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"first_output_sequence": "",
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"skip_examples": false,
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"output_suffix": "<|im_end|>\n",
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"input_suffix": "<|im_end|>\n",
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"system_suffix": "<|im_end|>\n",
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"user_alignment_message": "",
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"system_same_as_user": false,
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"last_system_sequence": "",
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"name": "Magnum ChatML"
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}
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```
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</details><br>
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## Axolotl config
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<details><summary>See axolotl config</summary>
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```yaml
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base_model: 01-ai/Yi-1.5-34B-32K
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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#trust_remote_code: true
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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datasets:
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- path: anthracite-org/stheno-filtered-v1.1
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type: sharegpt
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conversation: chatml
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- path: anthracite-org/kalo-opus-instruct-22k-no-refusal
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type: sharegpt
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conversation: chatml
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- path: anthracite-org/nopm_claude_writing_fixed
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type: sharegpt
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conversation: chatml
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- path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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type: sharegpt
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conversation: chatml
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- path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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type: sharegpt
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conversation: chatml
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chat_template: chatml
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shuffle_merged_datasets: true
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default_system_message: "You are an assistant that responds to the user."
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dataset_prepared_path: magnum-v2-34b-1.5-data
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val_set_size: 0.0
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output_dir: ./magnum-v2-34b-32k-r1
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sequence_len: 8192
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sample_packing: true
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eval_sample_packing: false
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pad_to_sequence_len:
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adapter:
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lora_model_dir:
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lora_r:
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lora_alpha:
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lora_dropout:
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lora_target_linear:
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lora_fan_in_fan_out:
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wandb_project: magnum-v2-34b-1.5-32k
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wandb_entity:
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wandb_watch:
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wandb_name: attempt-01
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wandb_log_model:
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gradient_accumulation_steps: 8
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micro_batch_size: 1
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num_epochs: 2
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.000006
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: unsloth
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 50
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evals_per_epoch:
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eval_table_size:
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eval_max_new_tokens:
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saves_per_epoch: 2
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debug:
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deepspeed: deepspeed_configs/zero3_bf16.json
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weight_decay: 0.05
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fsdp:
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fsdp_config:
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special_tokens:
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```
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</details><br>
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## Credits
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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.
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We would also like to thank all members of Anthracite who made this finetune possible.
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- [anthracite-org/Stheno-Data-Filtered](https://huggingface.co/datasets/anthracite-org/Stheno-Data-Filtered)
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- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
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- [lodrick-the-lafted/NopmWritingStruct](https://huggingface.co/datasets/lodrick-the-lafted/NopmWritingStruct)
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- [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned)
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- [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned)
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## Training
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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.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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## Safety
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...
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