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--- |
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language: |
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- en |
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license: agpl-3.0 |
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tags: |
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- chat |
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base_model: |
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- arcee-ai/Llama-3.1-SuperNova-Lite |
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datasets: |
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned |
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- Nitral-AI/Cybersecurity-ShareGPT |
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- Nitral-AI/Medical_Instruct-ShareGPT |
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- Nitral-AI/Olympiad_Math-ShareGPT |
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- anthracite-org/kalo_opus_misc_240827 |
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- NewEden/Claude-Instruct-5k |
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- lodrick-the-lafted/kalo-opus-instruct-3k-filtered |
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- anthracite-org/kalo-opus-instruct-22k-no-refusal |
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned |
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned |
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- anthracite-org/kalo_misc_part2 |
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- Nitral-AI/Creative_Writing-ShareGPT |
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- NewEden/Gryphe-Sonnet3.5-Charcard-Roleplay-unfiltered |
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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: arcee-ai/Llama-3.1-SuperNova-Lite |
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Tags: |
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- Chat |
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model-index: |
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- name: Baldur-8B |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: IFEval (0-Shot) |
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type: HuggingFaceH4/ifeval |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: inst_level_strict_acc and prompt_level_strict_acc |
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value: 47.82 |
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name: strict accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Delta-Vector/Baldur-8B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BBH (3-Shot) |
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type: BBH |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc_norm |
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value: 32.54 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Delta-Vector/Baldur-8B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MATH Lvl 5 (4-Shot) |
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type: hendrycks/competition_math |
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args: |
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num_few_shot: 4 |
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metrics: |
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- type: exact_match |
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value: 12.61 |
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name: exact match |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Delta-Vector/Baldur-8B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GPQA (0-shot) |
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type: Idavidrein/gpqa |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 6.94 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Delta-Vector/Baldur-8B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MuSR (0-shot) |
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type: TAUR-Lab/MuSR |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc_norm |
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value: 14.01 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Delta-Vector/Baldur-8B |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU-PRO (5-shot) |
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type: TIGER-Lab/MMLU-Pro |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 29.49 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Delta-Vector/Baldur-8B |
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name: Open LLM Leaderboard |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/Baldur-8B-GGUF |
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This is quantized version of [Delta-Vector/Baldur-8B](https://huggingface.co/Delta-Vector/Baldur-8B) created using llama.cpp |
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# Original Model Card |
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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. |
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# Quants |
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GGUF: https://huggingface.co/Delta-Vector/Baldur-8B-GGUF |
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EXL2: https://huggingface.co/Delta-Vector/Baldur-8B-EXL2 |
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## Prompting |
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Model has been Instruct tuned with the Llama-Instruct formatting. A typical input would look like this: |
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```py |
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"""<|begin_of_text|><|start_header_id|>system<|end_header_id|> |
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You are an AI built to rid the world of bonds and journeys!<|eot_id|><|start_header_id|>user<|end_header_id|> |
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Bro i just wanna know what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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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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• 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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• 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: arcee-ai/Llama-3.1-SuperNova-Lite |
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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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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_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: Gryphe/Sonnet3.5-SlimOrcaDedupCleaned |
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type: chat_template |
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- path: Nitral-AI/Cybersecurity-ShareGPT |
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type: chat_template |
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- path: Nitral-AI/Medical_Instruct-ShareGPT |
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type: chat_template |
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- path: Nitral-AI/Olympiad_Math-ShareGPT |
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type: chat_template |
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- path: anthracite-org/kalo_opus_misc_240827 |
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type: chat_template |
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- path: NewEden/Claude-Instruct-5k |
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type: chat_template |
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- path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered |
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type: chat_template |
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- path: anthracite-org/kalo-opus-instruct-22k-no-refusal |
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type: chat_template |
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- path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned |
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type: chat_template |
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- path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned |
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type: chat_template |
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- path: anthracite-org/kalo_misc_part2 |
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type: chat_template |
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- path: Nitral-AI/Creative_Writing-ShareGPT |
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type: chat_template |
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- path: NewEden/Gryphe-Sonnet3.5-Charcard-Roleplay-unfiltered |
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type: chat_template |
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chat_template: llama3 |
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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: prepared_dataset_memorycore |
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val_set_size: 0.0 |
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output_dir: ./henbane-8b-r3 |
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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: henbane-8b-r3 |
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wandb_entity: |
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wandb_watch: |
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wandb_name: henbane-8b-r3 |
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wandb_log_model: |
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gradient_accumulation_steps: 32 |
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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: 3e-5 |
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learning_rate: 1e-5 |
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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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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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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: 5 |
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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: /workspace/axolotl/deepspeed_configs/zero2.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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pad_token: <|finetune_right_pad_id|> |
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eos_token: <|eot_id|> |
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``` |
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</details><br> |
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## Credits |
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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.) |
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## Training |
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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. |
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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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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Delta-Vector__Baldur-8B) |
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| Metric |Value| |
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|-------------------|----:| |
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|Avg. |23.90| |
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|IFEval (0-Shot) |47.82| |
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|BBH (3-Shot) |32.54| |
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|MATH Lvl 5 (4-Shot)|12.61| |
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|GPQA (0-shot) | 6.94| |
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|MuSR (0-shot) |14.01| |
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|MMLU-PRO (5-shot) |29.49| |
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