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RichardErkhov/
DongfuJiang_-_prm_version2_subsample_no_ref_hf-gguf

Name: DongfuJiang_-_prm_version2_subsample_no_ref_hf-gguf
Availability: InStock

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prm_version2_subsample_no_ref_hf - GGUF

Model creator: https://huggingface.co/DongfuJiang/
Original model: https://huggingface.co/DongfuJiang/prm_version2_subsample_no_ref_hf/

Name	Quant method	Size
prm_version2_subsample_no_ref_hf.Q2_K.gguf	Q2_K	2.96GB
prm_version2_subsample_no_ref_hf.IQ3_XS.gguf	IQ3_XS	3.28GB
prm_version2_subsample_no_ref_hf.IQ3_S.gguf	IQ3_S	3.43GB
prm_version2_subsample_no_ref_hf.Q3_K_S.gguf	Q3_K_S	3.41GB
prm_version2_subsample_no_ref_hf.IQ3_M.gguf	IQ3_M	3.52GB
prm_version2_subsample_no_ref_hf.Q3_K.gguf	Q3_K	3.74GB
prm_version2_subsample_no_ref_hf.Q3_K_M.gguf	Q3_K_M	3.74GB
prm_version2_subsample_no_ref_hf.Q3_K_L.gguf	Q3_K_L	4.03GB
prm_version2_subsample_no_ref_hf.IQ4_XS.gguf	IQ4_XS	4.18GB
prm_version2_subsample_no_ref_hf.Q4_0.gguf	Q4_0	4.34GB
prm_version2_subsample_no_ref_hf.IQ4_NL.gguf	IQ4_NL	4.38GB
prm_version2_subsample_no_ref_hf.Q4_K_S.gguf	Q4_K_S	4.37GB
prm_version2_subsample_no_ref_hf.Q4_K.gguf	Q4_K	4.58GB
prm_version2_subsample_no_ref_hf.Q4_K_M.gguf	Q4_K_M	4.58GB
prm_version2_subsample_no_ref_hf.Q4_1.gguf	Q4_1	4.78GB
prm_version2_subsample_no_ref_hf.Q5_0.gguf	Q5_0	5.21GB
prm_version2_subsample_no_ref_hf.Q5_K_S.gguf	Q5_K_S	5.21GB
prm_version2_subsample_no_ref_hf.Q5_K.gguf	Q5_K	5.34GB
prm_version2_subsample_no_ref_hf.Q5_K_M.gguf	Q5_K_M	5.34GB
prm_version2_subsample_no_ref_hf.Q5_1.gguf	Q5_1	5.65GB
prm_version2_subsample_no_ref_hf.Q6_K.gguf	Q6_K	6.14GB
prm_version2_subsample_no_ref_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_version2_subsample_no_ref_hf results: []

prm_version2_subsample_no_ref_hf

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the prm_conversations_prm_version2_math+webinstructsub-mcq+webinstructsub-oe+apps_subsample_hf dataset. It achieves the following results on the evaluation set:

Loss: 0.1797

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
0.1954	0.2606	500	0.2134
0.1946	0.5213	1000	0.1948
0.1811	0.7819	1500	0.1824

Framework versions

Transformers 4.45.0
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3

RichardErkhov/DongfuJiang_-_prm_version2_subsample_no_ref_hf-gguf

Original model description:

prm_version2_subsample_no_ref_hf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
DongfuJiang_-_prm_version2_subsample_no_ref_hf-gguf