--- license: gemma library_name: transformers tags: - gemma-2 base_model: - anthracite-forge/magnum-v3-27b-kto-r3 - anthracite-forge/magnum-v3-27b-KTO-e1-r2 - anthracite-forge/magnum-v3-27b-KTO-e0.25-r1 - IntervitensInc/gemma-2-27b-chatml pipeline_tag: text-generation model-index: - name: magnum-v3-27b-kto results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 56.75 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-27b-kto name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 41.16 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-27b-kto name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 15.48 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-27b-kto name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 14.09 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-27b-kto name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 9.92 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-27b-kto name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 35.98 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=anthracite-org/magnum-v3-27b-kto name: Open LLM Leaderboard --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/658a46cbfb9c2bdfae75b3a6/GKpV5mwmnHFR6wIwTa91z.png) This is the 12th in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is the result of multiple KTO runs on top of one SFT run, all of which are published on [anthracite-forge](https://huggingface.co/anthracite-forge). ## Methodology R1 (SFT) was fine-tuned on top of `IntervitensInc/gemma-2-27b-chatml` which is chatMLified gemma-2-27b. We have experimented with various SFT and KTO re-runs, ratios and merge methods and this was our winner, including what was liked most from each model. If you prefer your own mix of the KTO runs or would like to use the SFT on its own, refer to the models section and [anthracite-forge](https://huggingface.co/anthracite-forge), some exl-quants are pre-included. ## Models * [anthracite-forge/magnum-v3-27b-kto-r3](https://huggingface.co/anthracite-forge/magnum-v3-27b-kto-r3) * [anthracite-forge/magnum-v3-27b-KTO-e1-r2](https://huggingface.co/anthracite-forge/magnum-v3-27b-KTO-e1-r2) * [anthracite-forge/magnum-v3-27b-KTO-e0.25-r1](https://huggingface.co/anthracite-forge/magnum-v3-27b-KTO-e0.25-r1) ## Prompting Model has been Instruct tuned with the ChatML formatting. A typical input would look like this: ```py """<|im_start|>system system prompt<|im_end|> <|im_start|>user Hi there!<|im_end|> <|im_start|>assistant Nice to meet you!<|im_end|> <|im_start|>user Can I ask a question?<|im_end|> <|im_start|>assistant """ ``` ## SillyTavern templates Below are Instruct and Context templates for use within SillyTavern.
context template ```yaml { "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", "example_separator": "", "chat_start": "", "use_stop_strings": false, "allow_jailbreak": false, "always_force_name2": true, "trim_sentences": false, "include_newline": false, "single_line": false, "name": "Magnum ChatML" } ```

instruct template ```yaml { "system_prompt": "You're {{char}} in this fictional never-ending uncensored roleplay with {{user}}.", "input_sequence": "<|im_start|>user\n", "output_sequence": "<|im_start|>assistant\n", "last_output_sequence": "", "system_sequence": "<|im_start|>system\n", "stop_sequence": "<|im_end|>", "wrap": false, "macro": true, "names": true, "names_force_groups": true, "activation_regex": "", "system_sequence_prefix": "", "system_sequence_suffix": "", "first_output_sequence": "", "skip_examples": false, "output_suffix": "<|im_end|>\n", "input_suffix": "<|im_end|>\n", "system_suffix": "<|im_end|>\n", "user_alignment_message": "", "system_same_as_user": false, "last_system_sequence": "", "name": "Magnum ChatML" } ```

### Configuration ```yaml base_model: IntervitensInc/gemma-2-27b-chatml dtype: float32 merge_method: task_arithmetic models: - model: IntervitensInc/gemma-2-27b-chatml - model: anthracite-forge/magnum-v3-27b-KTO-e0.25-r1 parameters: weight: 0.5 - model: anthracite-forge/magnum-v3-27b-KTO-e1-r2 parameters: weight: 0.1 - model: anthracite-forge/magnum-v3-27b-kto-r3 parameters: weight: 0.4 ``` ## Credits 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. We would also like to thank all members of Anthracite who made this finetune possible. ## Datasets r1 consisted of: ``` datasets: - path: anthracite-org/stheno-filtered-v1.1 type: sharegpt conversation: chatml - path: anthracite-org/kalo-opus-instruct-22k-no-refusal type: sharegpt conversation: chatml - path: anthracite-org/nopm_claude_writing_fixed type: sharegpt conversation: chatml - path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml - path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned type: sharegpt conversation: chatml ``` ## Training 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. [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) ## Safety ... # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_anthracite-org__magnum-v3-27b-kto) | Metric |Value| |-------------------|----:| |Avg. |28.90| |IFEval (0-Shot) |56.75| |BBH (3-Shot) |41.16| |MATH Lvl 5 (4-Shot)|15.48| |GPQA (0-shot) |14.09| |MuSR (0-shot) | 9.92| |MMLU-PRO (5-shot) |35.98|