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RichardErkhov/
DongfuJiang_-_prm_version3_full_hf-gguf

Name: DongfuJiang_-_prm_version3_full_hf-gguf
Availability: InStock

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prm_version3_full_hf - GGUF

Model creator: https://huggingface.co/DongfuJiang/
Original model: https://huggingface.co/DongfuJiang/prm_version3_full_hf/

Name	Quant method	Size
prm_version3_full_hf.Q2_K.gguf	Q2_K	2.96GB
prm_version3_full_hf.IQ3_XS.gguf	IQ3_XS	3.28GB
prm_version3_full_hf.IQ3_S.gguf	IQ3_S	3.43GB
prm_version3_full_hf.Q3_K_S.gguf	Q3_K_S	3.41GB
prm_version3_full_hf.IQ3_M.gguf	IQ3_M	3.52GB
prm_version3_full_hf.Q3_K.gguf	Q3_K	3.74GB
prm_version3_full_hf.Q3_K_M.gguf	Q3_K_M	3.74GB
prm_version3_full_hf.Q3_K_L.gguf	Q3_K_L	4.03GB
prm_version3_full_hf.IQ4_XS.gguf	IQ4_XS	4.18GB
prm_version3_full_hf.Q4_0.gguf	Q4_0	4.34GB
prm_version3_full_hf.IQ4_NL.gguf	IQ4_NL	4.38GB
prm_version3_full_hf.Q4_K_S.gguf	Q4_K_S	4.37GB
prm_version3_full_hf.Q4_K.gguf	Q4_K	4.58GB
prm_version3_full_hf.Q4_K_M.gguf	Q4_K_M	4.58GB
prm_version3_full_hf.Q4_1.gguf	Q4_1	4.78GB
prm_version3_full_hf.Q5_0.gguf	Q5_0	5.21GB
prm_version3_full_hf.Q5_K_S.gguf	Q5_K_S	5.21GB
prm_version3_full_hf.Q5_K.gguf	Q5_K	5.34GB
prm_version3_full_hf.Q5_K_M.gguf	Q5_K_M	5.34GB
prm_version3_full_hf.Q5_1.gguf	Q5_1	5.65GB
prm_version3_full_hf.Q6_K.gguf	Q6_K	6.14GB
prm_version3_full_hf.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags:

llama-factory
full
generated_from_trainer model-index:
name: prm_version3_full_hf results: []

prm_version3_full_hf

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the prm_conversations_prm_version3_math+webinstructsub-mcq+webinstructsub-oe+apps+gsm_mix_ref_hf dataset. It achieves the following results on the evaluation set:

Loss: 0.1166

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: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.1961	0.0461	500	0.2069
0.192	0.0921	1000	0.1930
0.1963	0.1382	1500	0.1833
0.1701	0.1843	2000	0.1748
0.1647	0.2303	2500	0.1687
0.1507	0.2764	3000	0.1630
0.1421	0.3225	3500	0.1579
0.1403	0.3685	4000	0.1528
0.1557	0.4146	4500	0.1485
0.1536	0.4607	5000	0.1441
0.1344	0.5067	5500	0.1399
0.1195	0.5528	6000	0.1355
0.1209	0.5989	6500	0.1316
0.137	0.6450	7000	0.1284
0.117	0.6910	7500	0.1253
0.116	0.7371	8000	0.1228
0.1259	0.7832	8500	0.1206
0.1147	0.8292	9000	0.1187
0.1175	0.8753	9500	0.1175
0.1117	0.9214	10000	0.1168
0.1133	0.9674	10500	0.1166

Framework versions

Transformers 4.45.0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

RichardErkhov/DongfuJiang_-_prm_version3_full_hf-gguf

Original model description:

prm_version3_full_hf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
DongfuJiang_-_prm_version3_full_hf-gguf