--- License: agpl-3.0 Language: - En Pipeline_tag: text-generation Base_model: arcee-ai/Llama-3.1-SuperNova-Lite Tags: - Chat license: agpl-3.0 datasets: - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - Nitral-AI/Cybersecurity-ShareGPT - Nitral-AI/Medical_Instruct-ShareGPT - Nitral-AI/Olympiad_Math-ShareGPT - anthracite-org/kalo_opus_misc_240827 - NewEden/Claude-Instruct-5k - lodrick-the-lafted/kalo-opus-instruct-3k-filtered - anthracite-org/kalo-opus-instruct-22k-no-refusal - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - anthracite-org/kalo_misc_part2 - Nitral-AI/Creative_Writing-ShareGPT - NewEden/Gryphe-Sonnet3.5-Charcard-Roleplay-unfiltered tags: - chat language: - en base_model: - arcee-ai/Llama-3.1-SuperNova-Lite --- ![](https://huggingface.co/Delta-Vector/Baldur-8B/resolve/main/Baldur.jpg) # These are GGUF quantizations for Baldur-8B, for the weights, go [here](https://huggingface.co/Delta-Vector/Baldur-8B) An finetune of the L3.1 instruct distill done by Arcee, The intent of this model is to have differing prose then my other releases, in my testing it has achieved this and avoiding using common -isms frequently and has a differing flavor then my other models. # Quants GGUF: https://huggingface.co/Delta-Vector/Baldur-8B-GGUF EXL2: https://huggingface.co/Delta-Vector/Baldur-8B-EXL2 ## Prompting Model has been Instruct tuned with the Llama-Instruct formatting. A typical input would look like this: ```py """<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an AI built to rid the world of bonds and journeys!<|eot_id|><|start_header_id|>user<|end_header_id|> Bro i just wanna know what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|> """ ``` ## System Prompting I would highly recommend using Sao10k's Euryale System prompt, But the "Roleplay Simple" system prompt provided within SillyTavern will work aswell. ``` Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. • Maintain the character persona but allow it to evolve with the story. • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. • All types of outputs are encouraged; respond accordingly to the narrative. • Include dialogues, actions, and thoughts in each response. • Utilize all five senses to describe scenarios within {{char}}'s dialogue. • Use emotional symbols such as "!" and "~" in appropriate contexts. • Incorporate onomatopoeia when suitable. • Allow time for {{user}} to respond with their own input, respecting their agency. • Act as secondary characters and NPCs as needed, and remove them when appropriate. • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. • Repetitive and monotonous outputs. • Positivity bias in your replies. • Being overly extreme or NSFW when the narrative context is inappropriate. Follow the instructions in , avoiding the items listed in . ``` ## Axolotl config
See axolotl config Axolotl version: `0.4.1` ```yaml base_model: arcee-ai/Llama-3.1-SuperNova-Lite model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer #trust_remote_code: true plugins: - axolotl.integrations.liger.LigerPlugin liger_rope: true liger_rms_norm: true liger_swiglu: true liger_fused_linear_cross_entropy: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: Gryphe/Sonnet3.5-SlimOrcaDedupCleaned type: chat_template - path: Nitral-AI/Cybersecurity-ShareGPT type: chat_template - path: Nitral-AI/Medical_Instruct-ShareGPT type: chat_template - path: Nitral-AI/Olympiad_Math-ShareGPT type: chat_template - path: anthracite-org/kalo_opus_misc_240827 type: chat_template - path: NewEden/Claude-Instruct-5k type: chat_template - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered type: chat_template - path: anthracite-org/kalo-opus-instruct-22k-no-refusal type: chat_template - path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned type: chat_template - path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned type: chat_template - path: anthracite-org/kalo_misc_part2 type: chat_template - path: Nitral-AI/Creative_Writing-ShareGPT type: chat_template - path: NewEden/Gryphe-Sonnet3.5-Charcard-Roleplay-unfiltered type: chat_template chat_template: llama3 shuffle_merged_datasets: true default_system_message: "You are an assistant that responds to the user." dataset_prepared_path: prepared_dataset_memorycore val_set_size: 0.0 output_dir: ./henbane-8b-r3 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: henbane-8b-r3 wandb_entity: wandb_watch: wandb_name: henbane-8b-r3 wandb_log_model: gradient_accumulation_steps: 32 micro_batch_size: 1 num_epochs: 2 optimizer: paged_adamw_8bit lr_scheduler: cosine #learning_rate: 3e-5 learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 5 evals_per_epoch: eval_table_size: eval_max_new_tokens: saves_per_epoch: 2 debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: pad_token: <|finetune_right_pad_id|> eos_token: <|eot_id|> ``` ## Credits Thank you to [Lucy Knada](https://huggingface.co/lucyknada), [Kalomaze](https://huggingface.co/kalomaze), [Kubernetes Bad](https://huggingface.co/kubernetes-bad) and the rest of [Anthracite](https://huggingface.co/anthracite-org) (But not Alpin.)

## Training The training was done for 2 epochs. I used 2 x [RTX 6000s](https://www.nvidia.com/en-us/design-visualization/rtx-6000/) GPUs graciously provided by [Kubernetes Bad](https://huggingface.co/kubernetes-bad) for the full-parameter fine-tuning of the model. [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)