--- library_name: transformers datasets: - argilla/distilabel-capybara-dpo-7k-binarized --- # CapyLake-7B-v2-laser This model is a finetune of [cognitivecomputations/WestLake-7B-v2-Laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser) on [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized)
![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/kx2uwS_kZ-rTAJiusSrAW.webp) [Built with Distilabel](https://github.com/argilla-io/distilabel)
## Process + Realigned the chat template to ChatML + Completed 1 Epoch + 5e-05 learning rate + Training time was about 2 hours on 1 H100 + Cost was ~$8 ## Code Example ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "macadeliccc/CapyLake-7B-v2-laser" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Create an idea for a TV show and write a short pilot script" inputs = tokenizer(text, return_tensors="pt") # Adding hyperparameters to the generation call outputs = model.generate( **inputs, max_new_tokens=4096, # Controls the maximum length of the new tokens created temperature=0.7, # Adjust for creativity (lower is less random) top_k=50, # Keeps the top k tokens for sampling top_p=0.95, # Uses nucleus sampling with this cumulative probability num_return_sequences=1, # Number of sequences to generate no_repeat_ngram_size=2, # Prevents repeating n-grams to ensure diversity early_stopping=True # Stops generation when all sequences reach the EOS token ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Other Capy Models SOLAR-10.7B-Capy-v1.0 is also on the way. There could be more depending on performance! ## Evaluations | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |-------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[CapyLake-7B-v2-laser](https://huggingface.co/macadeliccc/CapyLake-7B-v2-laser)| 44.34| 77.77| 68.47| 47.92| 59.62| ### AGIEval | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |28.35|± | 2.83| | | |acc_norm|25.98|± | 2.76| |agieval_logiqa_en | 0|acc |38.86|± | 1.91| | | |acc_norm|39.02|± | 1.91| |agieval_lsat_ar | 0|acc |25.22|± | 2.87| | | |acc_norm|24.35|± | 2.84| |agieval_lsat_lr | 0|acc |50.39|± | 2.22| | | |acc_norm|51.57|± | 2.22| |agieval_lsat_rc | 0|acc |65.06|± | 2.91| | | |acc_norm|63.94|± | 2.93| |agieval_sat_en | 0|acc |78.64|± | 2.86| | | |acc_norm|78.64|± | 2.86| |agieval_sat_en_without_passage| 0|acc |40.78|± | 3.43| | | |acc_norm|40.78|± | 3.43| |agieval_sat_math | 0|acc |33.64|± | 3.19| | | |acc_norm|30.45|± | 3.11| Average: 44.34% ### GPT4All | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |66.89|± | 1.38| | | |acc_norm|67.49|± | 1.37| |arc_easy | 0|acc |86.70|± | 0.70| | | |acc_norm|81.90|± | 0.79| |boolq | 1|acc |88.10|± | 0.57| |hellaswag | 0|acc |71.45|± | 0.45| | | |acc_norm|87.78|± | 0.33| |openbookqa | 0|acc |39.80|± | 2.19| | | |acc_norm|49.80|± | 2.24| |piqa | 0|acc |82.86|± | 0.88| | | |acc_norm|84.87|± | 0.84| |winogrande | 0|acc |84.45|± | 1.02| Average: 77.77% ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |53.98|± | 1.74| | | |mc2 |68.47|± | 1.53| Average: 68.47% ### Bigbench | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|59.47|± | 3.57| |bigbench_date_understanding | 0|multiple_choice_grade|64.50|± | 2.49| |bigbench_disambiguation_qa | 0|multiple_choice_grade|44.96|± | 3.10| |bigbench_geometric_shapes | 0|multiple_choice_grade|22.84|± | 2.22| | | |exact_str_match | 2.79|± | 0.87| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|30.80|± | 2.07| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|21.57|± | 1.56| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|56.67|± | 2.87| |bigbench_movie_recommendation | 0|multiple_choice_grade|51.60|± | 2.24| |bigbench_navigate | 0|multiple_choice_grade|51.00|± | 1.58| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|70.35|± | 1.02| |bigbench_ruin_names | 0|multiple_choice_grade|51.79|± | 2.36| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|35.97|± | 1.52| |bigbench_snarks | 0|multiple_choice_grade|79.01|± | 3.04| |bigbench_sports_understanding | 0|multiple_choice_grade|75.66|± | 1.37| |bigbench_temporal_sequences | 0|multiple_choice_grade|47.90|± | 1.58| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|23.84|± | 1.21| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.00|± | 0.92| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|56.67|± | 2.87| Average: 47.92% Average score: 59.62% Elapsed time: 01:57:56