--- license: apache-2.0 base_model: pszemraj/verysmol_llama-v7-KIx2 tags: - generated_from_trainer metrics: - accuracy inference: parameters: max_new_tokens: 64 do_sample: true temperature: 0.85 repetition_penalty: 1.35 no_repeat_ngram_size: 5 eta_cutoff: 0.0006 renormalize_logits: true widget: - text: My name is El Microondas the Wise and example_title: El Microondas - text: Kennesaw State University is a public example_title: Kennesaw State University - text: >- Bungie Studios is an American video game developer. They are most famous for developing the award winning Halo series of video games. They also made Destiny. The studio was founded example_title: Bungie - text: The Mona Lisa is a world-renowned painting created by example_title: Mona Lisa - text: >- The Harry Potter series, written by J.K. Rowling, begins with the book titled example_title: Harry Potter Series - text: >- Question: I have cities, but no houses. I have mountains, but no trees. I have water, but no fish. What am I? Answer: example_title: Riddle - text: The process of photosynthesis involves the conversion of example_title: Photosynthesis - text: >- Jane went to the store to buy some groceries. She picked up apples, oranges, and a loaf of bread. When she got home, she realized she forgot example_title: Story Continuation - text: >- Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and another train leaves Station B at 10:00 AM and travels at 80 mph, when will they meet if the distance between the stations is 300 miles? To determine example_title: Math Problem - text: In the context of computer programming, an algorithm is example_title: Algorithm Definition pipeline_tag: text-generation datasets: - JeanKaddour/minipile --- # BEE-spoke-data/verysmol_llama-v8-minipile_x2 This is still a work-in-progress and should be treated as such. ## Model description This is an autogressive smol language model. It generates text. It achieves the following results on the evaluation set: - Loss: 2.7521 - Accuracy: 0.4686 ## Intended uses & limitations Doing things. Limitations are that it is smol. Additionally, __. ## Data Most recent training run was on `JeanKaddour/minipile` for 2 epochs. Otherwise, please refer to the below quote: > UnFoRtUnAtElY We'rE UnAbLe tO ShArE DeTaIlS AbOuT ThE TrAiNiNg aNd tHe dAtAsEtS (eXtRaCtEd fRoM ThE OpEn wEb) DuE To tHe hIgHlY CoMpEtItIvE NaTuRe oF ThE FiElD. ## evals | **eval metrics** | | | -------------------------------------------------- | -------------------------- | | epoch | 2.0 | | eval_accuracy | 0.4685 | | eval_loss | 2.7521 | | eval_runtime | 0:00:03.89 | | eval_samples | 300 | | eval_samples_per_second | 77.049 | | eval_steps_per_second | 9.759 | | perplexity | 15.675 | ### harness > some improvements and some degradations over prev versions. May indicate the last dataset in curricula matters/needs to be chosen specifically `hf-causal-experimental (pretrained=BEE-spoke-data/verysmol_llama-v8-minipile_x2,revision=main,trust_remote_code=True,dtype='float'), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16` | Task |Version| Metric | Value | |Stderr| |--------------|------:|--------|-------:|---|-----:| |arc_easy | 0|acc | 0.3662|± |0.0099| | | |acc_norm| 0.3460|± |0.0098| |boolq | 1|acc | 0.6052|± |0.0085| |lambada_openai| 0|ppl |156.8153|± |6.5985| | | |acc | 0.2010|± |0.0056| |openbookqa | 0|acc | 0.1280|± |0.0150| | | |acc_norm| 0.2660|± |0.0198| |piqa | 0|acc | 0.5865|± |0.0115| | | |acc_norm| 0.5805|± |0.0115| |winogrande | 0|acc | 0.5217|± |0.0140| | Task |Version| Metric |Value | |Stderr| |-------------|------:|--------|-----:|---|-----:| |arc_challenge| 0|acc |0.1877|± |0.0114| | | |acc_norm|0.2235|± |0.0122| | Task |Version| Metric |Value | |Stderr| |---------|------:|--------|-----:|---|-----:| |hellaswag| 0|acc |0.2622|± |0.0088| | | |acc_norm|0.2777|± |0.0089| | Task |Version|Metric|Value | |Stderr| |-------------|------:|------|-----:|---|-----:| |truthfulqa_mc| 1|mc1 |0.2705|± |0.0156| | | |mc2 |0.4729|± |0.0155| --- ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00015 - train_batch_size: 8 - eval_batch_size: 8 - seed: 5404 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-07 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.7625 | 0.02 | 200 | 2.8982 | 0.4457 | | 2.7377 | 0.03 | 400 | 2.8812 | 0.4477 | | 2.6883 | 0.05 | 600 | 2.8774 | 0.4489 | | 2.7654 | 0.06 | 800 | 2.8811 | 0.4479 | | 2.744 | 0.08 | 1000 | 2.8838 | 0.4464 | | 2.6922 | 0.09 | 1200 | 2.8921 | 0.4461 | | 2.7416 | 0.11 | 1400 | 2.8930 | 0.4464 | | 2.7337 | 0.12 | 1600 | 2.8972 | 0.4465 | | 2.7046 | 0.14 | 1800 | 2.8933 | 0.4472 | | 2.673 | 0.15 | 2000 | 2.8926 | 0.4483 | ... | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.5155 | 1.88 | 24800 | 2.7524 | 0.4685 | | 2.5092 | 1.89 | 25000 | 2.7522 | 0.4686 | | 2.5093 | 1.91 | 25200 | 2.7523 | 0.4685 | | 2.4574 | 1.92 | 25400 | 2.7521 | 0.4686 | | 2.5137 | 1.94 | 25600 | 2.7522 | 0.4686 | | 2.4598 | 1.95 | 25800 | 2.7521 | 0.4686 | | 2.515 | 1.97 | 26000 | 2.7521 | 0.4685 | | 2.5429 | 1.98 | 26200 | 2.7521 | 0.4686 | | 2.4789 | 2.0 | 26400 | 2.7521 | 0.4686 |