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+
---
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License: agpl-3.0
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Language:
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- En
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Pipeline_tag: text-generation
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Base_model: nvidia/Mistral-NeMo-Minitron-8B-Base
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Tags:
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- Chat
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license: agpl-3.0
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datasets:
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- lodrick-the-lafted/kalo-opus-instruct-3k-filtered
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- anthracite-org/nopm_claude_writing_fixed
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/kalo_opus_misc_240827
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- anthracite-org/kalo_misc_part2
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tags:
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- chat
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---
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# Quants
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GGUF:
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EXL2:
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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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## System Prompting
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I would highly recommend using Sao10k's Euryale System prompt, But the "Roleplay Simple" system prompt provided within SillyTavern will work aswell.
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```
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Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.
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<Guidelines>
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• Write upto 200 words.
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• Maintain the character persona but allow it to evolve with the story.
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• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.
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• All types of outputs are encouraged; respond accordingly to the narrative.
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• Include dialogues, actions, and thoughts in each response.
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• Utilize all five senses to describe scenarios within {{char}}'s dialogue.
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• Use emotional symbols such as "!" and "~" in appropriate contexts.
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• Incorporate onomatopoeia when suitable.
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• Allow time for {{user}} to respond with their own input, respecting their agency.
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• Act as secondary characters and NPCs as needed, and remove them when appropriate.
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• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.
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</Guidelines>
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<Forbidden>
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• Writing more then 200 words.
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• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.
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• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.
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• Repetitive and monotonous outputs.
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• Positivity bias in your replies.
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• Being overly extreme or NSFW when the narrative context is inappropriate.
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</Forbidden>
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Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.
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```
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## Axolotl config
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<details><summary>See axolotl config</summary>
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Axolotl version: `0.4.1`
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```yaml
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base_model: Dans-DiscountModels/Mistral-NeMo-Minitron-8B-Base-ChatML
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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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_16k_llama_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: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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type: sharegpt
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conversation: chatml
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- path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
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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: anthracite-org/kalo_opus_misc_240827
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type: sharegpt
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conversation: chatml
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- path: anthracite-org/kalo_misc_part2
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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: false
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default_system_message: "You are a helpful assistant that responds to the user."
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dataset_prepared_path: /workspace/data/8b-nemo-fft-data
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val_set_size: 0.0
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output_dir: /workspace/data/8b-nemo-fft-out
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sequence_len: 16384
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sample_packing: true
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eval_sample_packing: false
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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: 8b-nemoprune-fft
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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: 2
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micro_batch_size: 2
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num_epochs: 4
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.00001
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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: /workspace/workspace/thing
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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: 10
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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: 1
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debug:
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deepspeed: deepspeed_configs/zero3_bf16.json
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weight_decay: 0.001
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fsdp:
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fsdp_config:
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special_tokens:
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pad_token: <pad>
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```
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</details><br>
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## Credits
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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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- [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned)
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- [lodrick-the-lafted/kalo-opus-instruct-3k-filtered](https://huggingface.co/datasets/lodrick-the-lafted/kalo-opus-instruct-3k-filtered)
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- [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed)
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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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- [anthracite-org/kalo_opus_misc_240827](https://huggingface.co/datasets/anthracite-org/kalo_opus_misc_240827)
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- [anthracite-org/kalo_misc_part2](https://huggingface.co/datasets/anthracite-org/kalo_misc_part2)
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- [anthracite-core/c2_logs_16k_llama_v1.1](https://huggingface.co/datasets/anthracite-core/c2_logs_16k_llama_v1.1)
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## Training
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The training was done for 2 epochs. We used 10 x [A40s](https://www.nvidia.com/en-us/data-center/a40/) GPUs graciously provided by [Kalomaze](https://huggingface.co/kalomaze) 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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Avoid misusing this model, or you’ll need a ‘clicker’ to reset reality. ;)
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## Musings
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One of the members of Anthracite had quite an interesting idea, to finetune a smaller model for 4 epochs at a lower Learning rate as quote "Smaller models learn slower" - [Kalomaze](https://huggingface.co/kalomaze) provided access to 10 X A40s and We finetuned what now is [Tor-8B]() for 2.5 epochs (and it's 4 Epoch version released as [Darkens-8B]()) and the result was quite impressive and the same configuration being used to train [Magnum=9B-V4] & [Odin-9B]. We also finetuned the model at above the 8192 context length to see if the model could "heal" in a way to a context length of 16384 with Needle tests coming soon ;)
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