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README.md
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---
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license: other
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license_name: mrl
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language:
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- en
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tags:
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- chat
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pipeline_tag: text-generation
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library_name: transformers
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/WvQykcYiK13x7sMI93T6e.png)
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## This repo contains GGUF quants of the model. If you need the original weights, please find them [here](https://huggingface.co/anthracite-org/magnum-v4-22b).
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This is 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 [Mistral-Small-Instruct-2409](https://huggingface.co/mistralai/Mistral-Small-Instruct-2409).
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## Prompting
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A typical input would look like this:
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```py
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<s>[INST] SYSTEM MESSAGE
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USER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]
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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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<details><summary>context template</summary>
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```yaml
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default SillyTavern template works fine
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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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default SillyTavern template works fine
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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: /workspace/models/Mistral-Small-Instruct-2409
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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hub_model_id: anthracite-core/magnum-v4-22b-r4
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hub_strategy: "all_checkpoints"
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push_dataset_to_hub:
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hf_use_auth_token: true
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_swiglu: true
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#liger_cross_entropy: true
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liger_fused_linear_cross_entropy: 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-core/c2_logs_32k_mistral-v3_v1.2_no_system
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type: custommistralv2v3
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- path: anthracite-core/kalo-opus-instruct-22k-no-refusal-no-system
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type: custommistralv2v3
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- path: anthracite-core/kalo-opus-instruct-3k-filtered-no-system
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type: custommistralv2v3
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- path: anthracite-org/nopm_claude_writing_fixed
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type: custommistralv2v3
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- path: anthracite-core/kalo_opus_misc_240827_no_system
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type: custommistralv2v3
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- path: anthracite-core/kalo_misc_part2_no_system
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type: custommistralv2v3
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#chat_template: mistral_v2v3
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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: /workspace/data/magnum-22b-data
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val_set_size: 0.0
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output_dir: /workspace/data/22b-r4-fft-out
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sequence_len: 32768
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sample_packing: true
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pad_to_sequence_len: true
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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: 22b-magnum-fft
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wandb_entity:
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wandb_watch:
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wandb_name: v4-r4-attempt-01
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wandb_log_model:
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gradient_accumulation_steps: 2
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micro_batch_size: 1
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num_epochs: 2
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.000004
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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: true
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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: 40
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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: 0.1
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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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## Datasets
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- [anthracite-core/c2_logs_32k_mistral-v3_v1.2_no_system](https://huggingface.co/datasets/anthracite-core/c2_logs_32k_mistral-v3_v1.2_no_system)
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- [anthracite-core/kalo-opus-instruct-22k-no-refusal-no-system](https://huggingface.co/datasets/anthracite-core/kalo-opus-instruct-22k-no-refusal-no-system)
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- [anthracite-core/kalo-opus-instruct-3k-filtered-no-system](https://huggingface.co/datasets/anthracite-core/kalo-opus-instruct-3k-filtered-no-system)
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- [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed)
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- [anthracite-core/kalo_opus_misc_240827_no_system](https://huggingface.co/datasets/anthracite-core/kalo_opus_misc_240827_no_system)
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- [anthracite-core/kalo_misc_part2_no_system](https://huggingface.co/datasets/anthracite-core/kalo_misc_part2_no_system)
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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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