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RichardErkhov/
Heejindo_-_rationale_model_e3_save5000_f4-gguf

Name: Heejindo_-_rationale_model_e3_save5000_f4-gguf
Rating: 2.5 (351 reviews)

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Quantization made by Richard Erkhov.

rationale_model_e3_save5000_f4 - GGUF

Name	Quant method	Size
rationale_model_e3_save5000_f4.Q2_K.gguf	Q2_K	0.54GB
rationale_model_e3_save5000_f4.IQ3_XS.gguf	IQ3_XS	0.58GB
rationale_model_e3_save5000_f4.IQ3_S.gguf	IQ3_S	0.6GB
rationale_model_e3_save5000_f4.Q3_K_S.gguf	Q3_K_S	0.6GB
rationale_model_e3_save5000_f4.IQ3_M.gguf	IQ3_M	0.61GB
rationale_model_e3_save5000_f4.Q3_K.gguf	Q3_K	0.64GB
rationale_model_e3_save5000_f4.Q3_K_M.gguf	Q3_K_M	0.64GB
rationale_model_e3_save5000_f4.Q3_K_L.gguf	Q3_K_L	0.68GB
rationale_model_e3_save5000_f4.IQ4_XS.gguf	IQ4_XS	0.7GB
rationale_model_e3_save5000_f4.Q4_0.gguf	Q4_0	0.72GB
rationale_model_e3_save5000_f4.IQ4_NL.gguf	IQ4_NL	0.72GB
rationale_model_e3_save5000_f4.Q4_K_S.gguf	Q4_K_S	0.72GB
rationale_model_e3_save5000_f4.Q4_K.gguf	Q4_K	0.75GB
rationale_model_e3_save5000_f4.Q4_K_M.gguf	Q4_K_M	0.75GB
rationale_model_e3_save5000_f4.Q4_1.gguf	Q4_1	0.77GB
rationale_model_e3_save5000_f4.Q5_0.gguf	Q5_0	0.83GB
rationale_model_e3_save5000_f4.Q5_K_S.gguf	Q5_K_S	0.83GB
rationale_model_e3_save5000_f4.Q5_K.gguf	Q5_K	0.85GB
rationale_model_e3_save5000_f4.Q5_K_M.gguf	Q5_K_M	0.85GB
rationale_model_e3_save5000_f4.Q5_1.gguf	Q5_1	0.89GB
rationale_model_e3_save5000_f4.Q6_K.gguf	Q6_K	0.95GB
rationale_model_e3_save5000_f4.Q8_0.gguf	Q8_0	1.23GB

Original model description:

library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags:

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss
1.7536	0.1907	1000	1.9369
1.3797	0.3813	2000	2.0320
1.0216	0.5720	3000	2.1529
0.6624	0.7626	4000	2.3760
0.3893	0.9533	5000	2.7429
0.1995	1.1439	6000	2.9766
0.1703	1.3346	7000	3.0843
0.1489	1.5253	8000	3.1774
0.1249	1.7159	9000	3.3298
0.1168	1.9066	10000	3.4572
0.0977	2.0972	11000	3.5885
0.0951	2.2879	12000	3.6941
0.092	2.4786	13000	3.7847
0.0894	2.6692	14000	3.9039
0.086	2.8599	15000	3.9903