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RichardErkhov/
Heejindo_-_rationale_model_e3_save5000_rp_f1-gguf

Name: Heejindo_-_rationale_model_e3_save5000_rp_f1-gguf
Rating: 2.5 (336 reviews)

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Quantization made by Richard Erkhov.

rationale_model_e3_save5000_rp_f1 - GGUF

Name	Quant method	Size
rationale_model_e3_save5000_rp_f1.Q2_K.gguf	Q2_K	0.54GB
rationale_model_e3_save5000_rp_f1.IQ3_XS.gguf	IQ3_XS	0.58GB
rationale_model_e3_save5000_rp_f1.IQ3_S.gguf	IQ3_S	0.6GB
rationale_model_e3_save5000_rp_f1.Q3_K_S.gguf	Q3_K_S	0.6GB
rationale_model_e3_save5000_rp_f1.IQ3_M.gguf	IQ3_M	0.61GB
rationale_model_e3_save5000_rp_f1.Q3_K.gguf	Q3_K	0.64GB
rationale_model_e3_save5000_rp_f1.Q3_K_M.gguf	Q3_K_M	0.64GB
rationale_model_e3_save5000_rp_f1.Q3_K_L.gguf	Q3_K_L	0.68GB
rationale_model_e3_save5000_rp_f1.IQ4_XS.gguf	IQ4_XS	0.7GB
rationale_model_e3_save5000_rp_f1.Q4_0.gguf	Q4_0	0.72GB
rationale_model_e3_save5000_rp_f1.IQ4_NL.gguf	IQ4_NL	0.72GB
rationale_model_e3_save5000_rp_f1.Q4_K_S.gguf	Q4_K_S	0.72GB
rationale_model_e3_save5000_rp_f1.Q4_K.gguf	Q4_K	0.75GB
rationale_model_e3_save5000_rp_f1.Q4_K_M.gguf	Q4_K_M	0.75GB
rationale_model_e3_save5000_rp_f1.Q4_1.gguf	Q4_1	0.77GB
rationale_model_e3_save5000_rp_f1.Q5_0.gguf	Q5_0	0.83GB
rationale_model_e3_save5000_rp_f1.Q5_K_S.gguf	Q5_K_S	0.83GB
rationale_model_e3_save5000_rp_f1.Q5_K.gguf	Q5_K	0.85GB
rationale_model_e3_save5000_rp_f1.Q5_K_M.gguf	Q5_K_M	0.85GB
rationale_model_e3_save5000_rp_f1.Q5_1.gguf	Q5_1	0.89GB
rationale_model_e3_save5000_rp_f1.Q6_K.gguf	Q6_K	0.95GB
rationale_model_e3_save5000_rp_f1.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
1.7719	0.1908	1000	1.9372
1.3947	0.3816	2000	1.9960
1.0148	0.5724	3000	2.1311
0.6554	0.7632	4000	2.4018
0.382	0.9540	5000	2.7497
0.1972	1.1448	6000	2.9048
0.1697	1.3356	7000	3.0675
0.1468	1.5264	8000	3.1610
0.1288	1.7172	9000	3.3265
0.1163	1.9080	10000	3.4370
0.0983	2.0988	11000	3.5843
0.097	2.2896	12000	3.6571
0.0932	2.4804	13000	3.7338
0.0892	2.6712	14000	3.8644
0.0867	2.8620	15000	3.9677