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--- |
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base_model: BEE-spoke-data/verysmol_llama-v11-KIx2 |
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datasets: |
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- BEE-spoke-data/knowledge-inoc-concat-v1 |
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inference: false |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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model_creator: BEE-spoke-data |
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model_name: verysmol_llama-v11-KIx2 |
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pipeline_tag: text-generation |
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quantized_by: afrideva |
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tags: |
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- generated_from_trainer |
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- gguf |
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- ggml |
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- quantized |
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- q2_k |
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- q3_k_m |
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- q4_k_m |
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- q5_k_m |
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- q6_k |
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- q8_0 |
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widget: |
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- example_title: El Microondas |
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text: My name is El Microondas the Wise and |
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- example_title: Kennesaw State University |
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text: Kennesaw State University is a public |
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- example_title: Bungie |
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text: Bungie Studios is an American video game developer. They are most famous for |
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developing the award winning Halo series of video games. They also made Destiny. |
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The studio was founded |
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- example_title: Mona Lisa |
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text: The Mona Lisa is a world-renowned painting created by |
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- example_title: Harry Potter Series |
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text: The Harry Potter series, written by J.K. Rowling, begins with the book titled |
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- example_title: Riddle |
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text: 'Question: I have cities, but no houses. I have mountains, but no trees. I |
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have water, but no fish. What am I? |
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Answer:' |
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- example_title: Photosynthesis |
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text: The process of photosynthesis involves the conversion of |
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- example_title: Story Continuation |
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text: Jane went to the store to buy some groceries. She picked up apples, oranges, |
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and a loaf of bread. When she got home, she realized she forgot |
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- example_title: Math Problem |
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text: 'Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, |
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and another train leaves Station B at 10:00 AM and travels at 80 mph, when will |
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they meet if the distance between the stations is 300 miles? |
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To determine' |
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- example_title: Algorithm Definition |
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text: In the context of computer programming, an algorithm is |
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--- |
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# BEE-spoke-data/verysmol_llama-v11-KIx2-GGUF |
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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) |
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| Name | Quant method | Size | |
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| ---- | ---- | ---- | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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| [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 | |
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## Original Model Card: |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# verysmol_llama-v11-KIx2 |
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## Model description |
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This model is a fine-tuned version of v10 (refinedweb-3m dedup) further trained for 2 epochs on KI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8876 |
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- Accuracy: 0.4502 |
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--- |
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## evals |
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`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` |
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| Task |Version| Metric | Value | |Stderr| |
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|--------------|------:|--------|-------:|---|-----:| |
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|arc_easy | 0|acc | 0.4024|± |0.0101| |
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| | |acc_norm| 0.3788|± |0.0100| |
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|boolq | 1|acc | 0.6199|± |0.0085| |
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|lambada_openai| 0|ppl |111.9939|± |4.6906| |
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| | |acc | 0.2354|± |0.0059| |
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|openbookqa | 0|acc | 0.1440|± |0.0157| |
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| | |acc_norm| 0.2760|± |0.0200| |
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|piqa | 0|acc | 0.5713|± |0.0115| |
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| | |acc_norm| 0.5664|± |0.0116| |
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|winogrande | 0|acc | 0.5201|± |0.0140| |
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| Task |Version| Metric |Value | |Stderr| |
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|-------------|------:|--------|-----:|---|-----:| |
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|arc_challenge| 0|acc |0.1971|± |0.0116| |
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| | |acc_norm|0.2278|± |0.0123| |
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| Task |Version| Metric |Value | |Stderr| |
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|---------|------:|--------|-----:|---|-----:| |
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|hellaswag| 0|acc |0.2618|± |0.0088| |
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| | |acc_norm|0.2797|± |0.0090| |
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| Task |Version|Metric|Value | |Stderr| |
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|-------------|------:|------|-----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |0.2509|± |0.0152| |
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| | |mc2 |0.4492|± |0.0156| |
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--- |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.00014 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 17514 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-06 |
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- lr_scheduler_type: inverse_sqrt |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 3.0681 | 0.03 | 150 | 3.0689 | 0.4259 | |
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| 3.0113 | 0.07 | 300 | 3.0433 | 0.4278 | |
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| 2.9468 | 0.1 | 450 | 3.0362 | 0.4288 | |
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| 3.0162 | 0.13 | 600 | 3.0148 | 0.4326 | |
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| 2.9531 | 0.17 | 750 | 3.0012 | 0.4341 | |
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| 2.9282 | 0.2 | 900 | 2.9923 | 0.4358 | |
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| 2.9485 | 0.23 | 1050 | 2.9845 | 0.4357 | |
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| 2.9365 | 0.27 | 1200 | 2.9749 | 0.4375 | |
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... |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.8215 | 1.7 | 7650 | 2.8943 | 0.4496 | |
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| 2.7714 | 1.74 | 7800 | 2.8914 | 0.4501 | |
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| 2.8132 | 1.77 | 7950 | 2.8913 | 0.4500 | |
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| 2.8505 | 1.8 | 8100 | 2.8906 | 0.4502 | |
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| 2.8294 | 1.84 | 8250 | 2.8901 | 0.4502 | |
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| 2.7977 | 1.87 | 8400 | 2.8891 | 0.4499 | |
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| 2.7501 | 1.9 | 8550 | 2.8878 | 0.4505 | |
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| 2.8038 | 1.94 | 8700 | 2.8883 | 0.4504 | |
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| 2.7547 | 1.97 | 8850 | 2.8876 | 0.4502 | |
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--- |
