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RichardErkhov/
Heejindo_-_rationale_model_e10_save5000_eos-gguf

Name: Heejindo_-_rationale_model_e10_save5000_eos-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

rationale_model_e10_save5000_eos - GGUF

Name	Quant method	Size
rationale_model_e10_save5000_eos.Q2_K.gguf	Q2_K	0.54GB
rationale_model_e10_save5000_eos.IQ3_XS.gguf	IQ3_XS	0.58GB
rationale_model_e10_save5000_eos.IQ3_S.gguf	IQ3_S	0.6GB
rationale_model_e10_save5000_eos.Q3_K_S.gguf	Q3_K_S	0.6GB
rationale_model_e10_save5000_eos.IQ3_M.gguf	IQ3_M	0.61GB
rationale_model_e10_save5000_eos.Q3_K.gguf	Q3_K	0.64GB
rationale_model_e10_save5000_eos.Q3_K_M.gguf	Q3_K_M	0.64GB
rationale_model_e10_save5000_eos.Q3_K_L.gguf	Q3_K_L	0.68GB
rationale_model_e10_save5000_eos.IQ4_XS.gguf	IQ4_XS	0.7GB
rationale_model_e10_save5000_eos.Q4_0.gguf	Q4_0	0.72GB
rationale_model_e10_save5000_eos.IQ4_NL.gguf	IQ4_NL	0.72GB
rationale_model_e10_save5000_eos.Q4_K_S.gguf	Q4_K_S	0.72GB
rationale_model_e10_save5000_eos.Q4_K.gguf	Q4_K	0.75GB
rationale_model_e10_save5000_eos.Q4_K_M.gguf	Q4_K_M	0.75GB
rationale_model_e10_save5000_eos.Q4_1.gguf	Q4_1	0.77GB
rationale_model_e10_save5000_eos.Q5_0.gguf	Q5_0	0.83GB
rationale_model_e10_save5000_eos.Q5_K_S.gguf	Q5_K_S	0.83GB
rationale_model_e10_save5000_eos.Q5_K.gguf	Q5_K	0.85GB
rationale_model_e10_save5000_eos.Q5_K_M.gguf	Q5_K_M	0.85GB
rationale_model_e10_save5000_eos.Q5_1.gguf	Q5_1	0.89GB
rationale_model_e10_save5000_eos.Q6_K.gguf	Q6_K	0.95GB
rationale_model_e10_save5000_eos.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:

learning_rate: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training Loss	Epoch	Step	Validation Loss
1.0611	0.4769	5000	2.0876
0.3274	0.9538	10000	2.7190
0.1866	1.4308	15000	2.9944
0.1541	1.9077	20000	3.1986
0.1317	2.3846	25000	3.2512
0.1239	2.8615	30000	3.4157
0.1082	3.3384	35000	3.4850
0.1058	3.8153	40000	3.5546
0.0903	4.2923	45000	3.6930
0.0888	4.7692	50000	3.7675
0.0705	5.2461	55000	3.8942
0.071	5.7230	60000	4.0015
0.0547	6.1999	65000	4.1481
0.0554	6.6768	70000	4.2596
0.0463	7.1538	75000	4.3939
0.0451	7.6307	80000	4.4723
0.0395	8.1076	85000	4.6841
0.0395	8.5845	90000	4.7395
0.0361	9.0614	95000	4.8502
0.0368	9.5383	100000	4.8884