--- language: - en license: apache-2.0 library_name: transformers model-index: - name: SOLAR-math-2x10.7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.43 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.31 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 66.9 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 64.21 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 83.35 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/SOLAR-math-2x10.7b name: Open LLM Leaderboard --- # 🌞🚀 SOLAR-math-2x10.7_19B + This model is part of MoE experimentation. The other solar models in the collection are available [here](https://huggingface.co/collections/macadeliccc/solar-moe-65a2d28e3581a68c41381d5b) + If you like this model, version 2 is even better! It is competitve with GPT-3.5 Turbo and Gemini Pro. It exceeds the scores of Mixtral8x7b [macadeliccc/SOLAR-math-2x10.7b-v0.2](https://huggingface.co/macadeliccc/SOLAR-math-2x10.7b-v0.2) ![solar](solar-2.png) ## 🌅 Code Example Example also available in [colab](https://colab.research.google.com/drive/10FWCLODU_EFclVOFOlxNYMmSiLilGMBZ?usp=sharing) ```python from transformers import AutoModelForCausalLM, AutoTokenizer def generate_response(prompt): """ Generate a response from the model based on the input prompt. Args: prompt (str): Prompt for the model. Returns: str: The generated response from the model. """ # Tokenize the input prompt inputs = tokenizer(prompt, return_tensors="pt") # Generate output tokens outputs = model.generate(**inputs, max_new_tokens=512, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id) # Decode the generated tokens to a string response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Load the model and tokenizer model_id = "macadeliccc/SOLAR-math-2x10.7b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) prompt = "Explain the proof of Fermat's Last Theorem and its implications in number theory." print("Response:") print(generate_response(prompt), "\n") ``` ## Evaluations | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |---------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[SOLAR-math-2x10.7b](https://huggingface.co/macadeliccc/SOLAR-math-2x10.7b)| 47.2| 75.18| 64.73| 45.15| 58.07| ### AGIEval | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |30.31|± | 2.89| | | |acc_norm|30.31|± | 2.89| |agieval_logiqa_en | 0|acc |43.78|± | 1.95| | | |acc_norm|43.93|± | 1.95| |agieval_lsat_ar | 0|acc |21.74|± | 2.73| | | |acc_norm|19.13|± | 2.60| |agieval_lsat_lr | 0|acc |57.25|± | 2.19| | | |acc_norm|56.47|± | 2.20| |agieval_lsat_rc | 0|acc |68.77|± | 2.83| | | |acc_norm|68.03|± | 2.85| |agieval_sat_en | 0|acc |78.16|± | 2.89| | | |acc_norm|79.13|± | 2.84| |agieval_sat_en_without_passage| 0|acc |47.57|± | 3.49| | | |acc_norm|44.66|± | 3.47| |agieval_sat_math | 0|acc |41.36|± | 3.33| | | |acc_norm|35.91|± | 3.24| Average: 47.2% ### GPT4All | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |59.22|± | 1.44| | | |acc_norm|61.43|± | 1.42| |arc_easy | 0|acc |84.26|± | 0.75| | | |acc_norm|83.63|± | 0.76| |boolq | 1|acc |88.69|± | 0.55| |hellaswag | 0|acc |65.98|± | 0.47| | | |acc_norm|84.29|± | 0.36| |openbookqa | 0|acc |34.20|± | 2.12| | | |acc_norm|47.20|± | 2.23| |piqa | 0|acc |81.83|± | 0.90| | | |acc_norm|82.59|± | 0.88| |winogrande | 0|acc |78.45|± | 1.16| Average: 75.18% ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |48.47|± | 1.75| | | |mc2 |64.73|± | 1.53| Average: 64.73% ### Bigbench | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|61.05|± | 3.55| |bigbench_date_understanding | 0|multiple_choice_grade|68.56|± | 2.42| |bigbench_disambiguation_qa | 0|multiple_choice_grade|35.27|± | 2.98| |bigbench_geometric_shapes | 0|multiple_choice_grade|31.20|± | 2.45| | | |exact_str_match | 0.00|± | 0.00| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|30.00|± | 2.05| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|23.43|± | 1.60| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|46.00|± | 2.88| |bigbench_movie_recommendation | 0|multiple_choice_grade|35.60|± | 2.14| |bigbench_navigate | 0|multiple_choice_grade|57.50|± | 1.56| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|55.80|± | 1.11| |bigbench_ruin_names | 0|multiple_choice_grade|45.98|± | 2.36| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|40.58|± | 1.56| |bigbench_snarks | 0|multiple_choice_grade|66.85|± | 3.51| |bigbench_sports_understanding | 0|multiple_choice_grade|71.40|± | 1.44| |bigbench_temporal_sequences | 0|multiple_choice_grade|56.40|± | 1.57| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|24.00|± | 1.21| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|17.09|± | 0.90| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|46.00|± | 2.88| Average: 45.15% Average score: 58.07% Elapsed time: 04:05:27 ### 📚 Citations ```bibtex @misc{kim2023solar, title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling}, author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim}, year={2023}, eprint={2312.15166}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__SOLAR-math-2x10.7b) | Metric |Value| |---------------------------------|----:| |Avg. |73.37| |AI2 Reasoning Challenge (25-Shot)|68.43| |HellaSwag (10-Shot) |86.31| |MMLU (5-Shot) |66.90| |TruthfulQA (0-shot) |64.21| |Winogrande (5-shot) |83.35| |GSM8k (5-shot) |71.04|