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RichardErkhov/
tongliuphysics_-_Llama0-3-8b-ultra-p-0.025-gguf

Name: tongliuphysics_-_Llama0-3-8b-ultra-p-0.025-gguf
Availability: InStock

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Llama0-3-8b-ultra-p-0.025 - GGUF

Model creator: https://huggingface.co/tongliuphysics/
Original model: https://huggingface.co/tongliuphysics/Llama0-3-8b-ultra-p-0.025/

Name	Quant method	Size
Llama0-3-8b-ultra-p-0.025.Q2_K.gguf	Q2_K	2.96GB
Llama0-3-8b-ultra-p-0.025.IQ3_XS.gguf	IQ3_XS	3.28GB
Llama0-3-8b-ultra-p-0.025.IQ3_S.gguf	IQ3_S	3.43GB
Llama0-3-8b-ultra-p-0.025.Q3_K_S.gguf	Q3_K_S	3.41GB
Llama0-3-8b-ultra-p-0.025.IQ3_M.gguf	IQ3_M	3.52GB
Llama0-3-8b-ultra-p-0.025.Q3_K.gguf	Q3_K	3.74GB
Llama0-3-8b-ultra-p-0.025.Q3_K_M.gguf	Q3_K_M	3.74GB
Llama0-3-8b-ultra-p-0.025.Q3_K_L.gguf	Q3_K_L	4.03GB
Llama0-3-8b-ultra-p-0.025.IQ4_XS.gguf	IQ4_XS	4.18GB
Llama0-3-8b-ultra-p-0.025.Q4_0.gguf	Q4_0	4.34GB
Llama0-3-8b-ultra-p-0.025.IQ4_NL.gguf	IQ4_NL	4.38GB
Llama0-3-8b-ultra-p-0.025.Q4_K_S.gguf	Q4_K_S	4.37GB
Llama0-3-8b-ultra-p-0.025.Q4_K.gguf	Q4_K	4.58GB
Llama0-3-8b-ultra-p-0.025.Q4_K_M.gguf	Q4_K_M	4.58GB
Llama0-3-8b-ultra-p-0.025.Q4_1.gguf	Q4_1	4.78GB
Llama0-3-8b-ultra-p-0.025.Q5_0.gguf	Q5_0	5.21GB
Llama0-3-8b-ultra-p-0.025.Q5_K_S.gguf	Q5_K_S	5.21GB
Llama0-3-8b-ultra-p-0.025.Q5_K.gguf	Q5_K	5.34GB
Llama0-3-8b-ultra-p-0.025.Q5_K_M.gguf	Q5_K_M	5.34GB
Llama0-3-8b-ultra-p-0.025.Q5_1.gguf	Q5_1	5.65GB
Llama0-3-8b-ultra-p-0.025.Q6_K.gguf	Q6_K	6.14GB
Llama0-3-8b-ultra-p-0.025.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers license: llama3 base_model: meta-llama/Meta-Llama-3-8B-Instruct tags:

trl
dpo
generated_from_trainer model-index:
name: Llama0-3-8b-ultra-p-0.025 results: []

Llama0-3-8b-ultra-p-0.025

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5336
Rewards/chosen: -0.9176
Rewards/rejected: -1.7954
Rewards/accuracies: 0.7266
Rewards/margins: 0.8778
Logps/rejected: -444.2013
Logps/chosen: -348.3104
Logits/rejected: 0.6139
Logits/chosen: 0.4767

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-07
train_batch_size: 2
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6219	0.2060	100	0.6185	-0.3528	-0.5814	0.6797	0.2287	-322.8062	-291.8294	0.3035	0.2418
0.5957	0.4119	200	0.5909	-0.4280	-0.7949	0.6875	0.3669	-344.1528	-299.3566	0.2675	0.1885
0.5718	0.6179	300	0.5747	-0.5426	-1.0432	0.6797	0.5006	-368.9808	-310.8154	0.4283	0.3112
0.5604	0.8239	400	0.5587	-0.5860	-1.1683	0.7031	0.5824	-381.4986	-315.1523	0.4728	0.3510
0.5212	1.0299	500	0.5435	-0.7801	-1.5211	0.7344	0.7410	-416.7767	-334.5627	0.5275	0.3937
0.4671	1.2358	600	0.5421	-0.9911	-1.8546	0.7109	0.8635	-450.1224	-355.6647	0.6644	0.5299
0.4778	1.4418	700	0.5348	-0.9156	-1.7871	0.7266	0.8715	-443.3745	-348.1178	0.6300	0.4953
0.4791	1.6478	800	0.5330	-0.9374	-1.8128	0.7266	0.8754	-445.9473	-350.2950	0.6640	0.5278
0.4831	1.8538	900	0.5340	-0.9312	-1.8200	0.7266	0.8888	-446.6618	-349.6732	0.6213	0.4847

Framework versions

Transformers 4.45.1
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.20.0

RichardErkhov/tongliuphysics_-_Llama0-3-8b-ultra-p-0.025-gguf

Original model description:

Llama0-3-8b-ultra-p-0.025

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
tongliuphysics_-_Llama0-3-8b-ultra-p-0.025-gguf