BEE-spoke-data/smol_llama-220M-bees-internal-GGUF
Quantized GGUF model files for smol_llama-220M-bees-internal from BEE-spoke-data
| Name | Quant method | Size |
|---|---|---|
| smol_llama-220m-bees-internal.fp16.gguf | fp16 | 436.50 MB |
| smol_llama-220m-bees-internal.q2_k.gguf | q2_k | 94.43 MB |
| smol_llama-220m-bees-internal.q3_k_m.gguf | q3_k_m | 114.65 MB |
| smol_llama-220m-bees-internal.q4_k_m.gguf | q4_k_m | 137.58 MB |
| smol_llama-220m-bees-internal.q5_k_m.gguf | q5_k_m | 157.91 MB |
| smol_llama-220m-bees-internal.q6_k.gguf | q6_k | 179.52 MB |
| smol_llama-220m-bees-internal.q8_0.gguf | q8_0 | 232.28 MB |
Original Model Card:
smol_llama-220M-bees-internal
This model is a fine-tuned version of BEE-spoke-data/smol_llama-220M-GQA on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.6892
- Accuracy: 0.4610
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 27634
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.0959 | 0.1 | 50 | 2.9671 | 0.4245 |
| 2.9975 | 0.19 | 100 | 2.8691 | 0.4371 |
| 2.8938 | 0.29 | 150 | 2.8271 | 0.4419 |
| 2.9027 | 0.39 | 200 | 2.7973 | 0.4457 |
| 2.8983 | 0.49 | 250 | 2.7719 | 0.4489 |
| 2.8789 | 0.58 | 300 | 2.7519 | 0.4515 |
| 2.8672 | 0.68 | 350 | 2.7366 | 0.4535 |
| 2.8369 | 0.78 | 400 | 2.7230 | 0.4558 |
| 2.8271 | 0.88 | 450 | 2.7118 | 0.4569 |
| 2.7775 | 0.97 | 500 | 2.7034 | 0.4587 |
| 2.671 | 1.07 | 550 | 2.6996 | 0.4592 |
| 2.695 | 1.17 | 600 | 2.6965 | 0.4598 |
| 2.6962 | 1.27 | 650 | 2.6934 | 0.4601 |
| 2.6034 | 1.36 | 700 | 2.6916 | 0.4605 |
| 2.716 | 1.46 | 750 | 2.6901 | 0.4609 |
| 2.6968 | 1.56 | 800 | 2.6896 | 0.4608 |
| 2.6626 | 1.66 | 850 | 2.6893 | 0.4609 |
| 2.6881 | 1.75 | 900 | 2.6891 | 0.4610 |
| 2.7339 | 1.85 | 950 | 2.6891 | 0.4610 |
| 2.6729 | 1.95 | 1000 | 2.6892 | 0.4610 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0
- Datasets 2.16.1
- Tokenizers 0.15.0