--- base_model: BEE-spoke-data/verysmol_llama-v11-KIx2 datasets: - BEE-spoke-data/knowledge-inoc-concat-v1 inference: false license: apache-2.0 metrics: - accuracy model_creator: BEE-spoke-data model_name: verysmol_llama-v11-KIx2 pipeline_tag: text-generation quantized_by: afrideva tags: - generated_from_trainer - gguf - ggml - quantized - q2_k - q3_k_m - q4_k_m - q5_k_m - q6_k - q8_0 widget: - example_title: El Microondas text: My name is El Microondas the Wise and - example_title: Kennesaw State University text: Kennesaw State University is a public - example_title: Bungie 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: Mona Lisa text: The Mona Lisa is a world-renowned painting created by - example_title: Harry Potter Series text: The Harry Potter series, written by J.K. Rowling, begins with the book titled - example_title: Riddle 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: Photosynthesis text: The process of photosynthesis involves the conversion of - example_title: Story Continuation 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: Math Problem 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: Algorithm Definition text: In the context of computer programming, an algorithm is --- # BEE-spoke-data/verysmol_llama-v11-KIx2-GGUF Quantized GGUF model files for [verysmol_llama-v11-KIx2](https://huggingface.co/BEE-spoke-data/verysmol_llama-v11-KIx2) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data) | Name | Quant method | Size | | ---- | ---- | ---- | | [verysmol_llama-v11-kix2.fp16.gguf](https://huggingface.co/afrideva/verysmol_llama-v11-KIx2-GGUF/resolve/main/verysmol_llama-v11-kix2.fp16.gguf) | fp16 | 116.89 MB | | [verysmol_llama-v11-kix2.q2_k.gguf](https://huggingface.co/afrideva/verysmol_llama-v11-KIx2-GGUF/resolve/main/verysmol_llama-v11-kix2.q2_k.gguf) | q2_k | 30.14 MB | | [verysmol_llama-v11-kix2.q3_k_m.gguf](https://huggingface.co/afrideva/verysmol_llama-v11-KIx2-GGUF/resolve/main/verysmol_llama-v11-kix2.q3_k_m.gguf) | q3_k_m | 33.71 MB | | [verysmol_llama-v11-kix2.q4_k_m.gguf](https://huggingface.co/afrideva/verysmol_llama-v11-KIx2-GGUF/resolve/main/verysmol_llama-v11-kix2.q4_k_m.gguf) | q4_k_m | 38.34 MB | | [verysmol_llama-v11-kix2.q5_k_m.gguf](https://huggingface.co/afrideva/verysmol_llama-v11-KIx2-GGUF/resolve/main/verysmol_llama-v11-kix2.q5_k_m.gguf) | q5_k_m | 43.21 MB | | [verysmol_llama-v11-kix2.q6_k.gguf](https://huggingface.co/afrideva/verysmol_llama-v11-KIx2-GGUF/resolve/main/verysmol_llama-v11-kix2.q6_k.gguf) | q6_k | 48.39 MB | | [verysmol_llama-v11-kix2.q8_0.gguf](https://huggingface.co/afrideva/verysmol_llama-v11-KIx2-GGUF/resolve/main/verysmol_llama-v11-kix2.q8_0.gguf) | q8_0 | 62.45 MB | ## Original Model Card: # verysmol_llama-v11-KIx2 ## Model description This model is a fine-tuned version of v10 (refinedweb-3m dedup) further trained for 2 epochs on KI dataset. It achieves the following results on the evaluation set: - Loss: 2.8876 - Accuracy: 0.4502 --- ## evals `hf-causal-experimental (pretrained=pszemraj/verysmol_llama-v11-KIx2,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 | 0.4024|± |0.0101| | | |acc_norm| 0.3788|± |0.0100| |boolq | 1|acc | 0.6199|± |0.0085| |lambada_openai| 0|ppl |111.9939|± |4.6906| | | |acc | 0.2354|± |0.0059| |openbookqa | 0|acc | 0.1440|± |0.0157| | | |acc_norm| 0.2760|± |0.0200| |piqa | 0|acc | 0.5713|± |0.0115| | | |acc_norm| 0.5664|± |0.0116| |winogrande | 0|acc | 0.5201|± |0.0140| | Task |Version| Metric |Value | |Stderr| |-------------|------:|--------|-----:|---|-----:| |arc_challenge| 0|acc |0.1971|± |0.0116| | | |acc_norm|0.2278|± |0.0123| | Task |Version| Metric |Value | |Stderr| |---------|------:|--------|-----:|---|-----:| |hellaswag| 0|acc |0.2618|± |0.0088| | | |acc_norm|0.2797|± |0.0090| | Task |Version|Metric|Value | |Stderr| |-------------|------:|------|-----:|---|-----:| |truthfulqa_mc| 1|mc1 |0.2509|± |0.0152| | | |mc2 |0.4492|± |0.0156| --- ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00014 - train_batch_size: 16 - eval_batch_size: 16 - seed: 17514 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-06 - lr_scheduler_type: inverse_sqrt - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.0681 | 0.03 | 150 | 3.0689 | 0.4259 | | 3.0113 | 0.07 | 300 | 3.0433 | 0.4278 | | 2.9468 | 0.1 | 450 | 3.0362 | 0.4288 | | 3.0162 | 0.13 | 600 | 3.0148 | 0.4326 | | 2.9531 | 0.17 | 750 | 3.0012 | 0.4341 | | 2.9282 | 0.2 | 900 | 2.9923 | 0.4358 | | 2.9485 | 0.23 | 1050 | 2.9845 | 0.4357 | | 2.9365 | 0.27 | 1200 | 2.9749 | 0.4375 | ... | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8215 | 1.7 | 7650 | 2.8943 | 0.4496 | | 2.7714 | 1.74 | 7800 | 2.8914 | 0.4501 | | 2.8132 | 1.77 | 7950 | 2.8913 | 0.4500 | | 2.8505 | 1.8 | 8100 | 2.8906 | 0.4502 | | 2.8294 | 1.84 | 8250 | 2.8901 | 0.4502 | | 2.7977 | 1.87 | 8400 | 2.8891 | 0.4499 | | 2.7501 | 1.9 | 8550 | 2.8878 | 0.4505 | | 2.8038 | 1.94 | 8700 | 2.8883 | 0.4504 | | 2.7547 | 1.97 | 8850 | 2.8876 | 0.4502 | ---