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RichardErkhov/thrunlab_-_sparse_llama_7b_hf_refined_web_70p_2024-03-25-4bits
thrunlab_-_sparse_llama_7b_hf_refined_web_70p_2024-03-25-4bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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Updated Jan 25, 2025
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From the Hugging Face model README
Quantization made by Richard Erkhov.
sparse_llama_7b_hf_refined_web_70p_2024-03-25 - bnb 4bits
base_model: meta-llama/Llama-2-7b-hf tags:
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the None dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3676 | 0.01 | 25 | 2.5933 |
| 2.3572 | 0.02 | 50 | 2.5793 |
| 2.4503 | 0.02 | 75 | 2.5568 |
| 2.3803 | 0.03 | 100 | 2.5265 |
| 2.4451 | 0.04 | 125 | 2.4951 |
| 2.2793 | 0.05 | 150 | 2.4778 |
| 2.2444 | 0.06 | 175 | 2.4667 |
| 2.406 | 0.06 | 200 | 2.4572 |
| 2.3583 | 0.07 | 225 | 2.4508 |
| 2.3262 | 0.08 | 250 | 2.4538 |
| 2.258 | 0.09 | 275 | 2.4476 |
| 2.2841 | 0.1 | 300 | 2.4456 |
| 2.3232 | 0.1 | 325 | 2.4379 |
| 2.2974 | 0.11 | 350 | 2.4353 |
| 2.2216 | 0.12 | 375 | 2.4379 |
| 2.3179 | 0.13 | 400 | 2.4340 |
| 2.3006 | 0.14 | 425 | 2.4333 |
| 2.2603 | 0.14 | 450 | 2.4333 |
| 2.3371 | 0.15 | 475 | 2.4384 |
| 2.3453 | 0.16 | 500 | 2.4328 |
| 2.254 | 0.17 | 525 | 2.4306 |
| 2.2423 | 0.18 | 550 | 2.4298 |
| 2.3666 | 0.18 | 575 | 2.4293 |
| 2.259 | 0.19 | 600 | 2.4298 |
| 2.2786 | 0.2 | 625 | 2.4290 |
| 2.3493 | 0.21 | 650 | 2.4275 |
| 2.2532 | 0.22 | 675 | 2.4255 |
| 2.2698 | 0.22 | 700 | 2.4233 |
| 2.2949 | 0.23 | 725 | 2.4277 |
| 2.1918 | 0.24 | 750 | 2.4268 |
| 2.2762 | 0.25 | 775 | 2.4243 |
| 2.3221 | 0.26 | 800 | 2.4256 |
| 2.278 | 0.26 | 825 | 2.4273 |
| 2.2406 | 0.27 | 850 | 2.4223 |
| 2.2466 | 0.28 | 875 | 2.4252 |
| 2.2199 | 0.29 | 900 | 2.4247 |
| 2.4064 | 0.3 | 925 | 2.4259 |
| 2.3672 | 0.3 | 950 | 2.4237 |
| 2.3096 | 0.31 | 975 | 2.4226 |
| 2.1979 | 0.32 | 1000 | 2.4257 |