You can deploy this model and start earning money today!

RichardErkhov/
mlfoundations-dev_-_hp_ablations_llama3_epoch3-gguf

Name: mlfoundations-dev_-_hp_ablations_llama3_epoch3-gguf
Availability: InStock

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

hp_ablations_llama3_epoch3 - GGUF

Model creator: https://huggingface.co/mlfoundations-dev/
Original model: https://huggingface.co/mlfoundations-dev/hp_ablations_llama3_epoch3/

Name	Quant method	Size
hp_ablations_llama3_epoch3.Q2_K.gguf	Q2_K	2.96GB
hp_ablations_llama3_epoch3.IQ3_XS.gguf	IQ3_XS	3.28GB
hp_ablations_llama3_epoch3.IQ3_S.gguf	IQ3_S	3.43GB
hp_ablations_llama3_epoch3.Q3_K_S.gguf	Q3_K_S	3.41GB
hp_ablations_llama3_epoch3.IQ3_M.gguf	IQ3_M	3.52GB
hp_ablations_llama3_epoch3.Q3_K.gguf	Q3_K	3.74GB
hp_ablations_llama3_epoch3.Q3_K_M.gguf	Q3_K_M	3.74GB
hp_ablations_llama3_epoch3.Q3_K_L.gguf	Q3_K_L	4.03GB
hp_ablations_llama3_epoch3.IQ4_XS.gguf	IQ4_XS	4.18GB
hp_ablations_llama3_epoch3.Q4_0.gguf	Q4_0	4.34GB
hp_ablations_llama3_epoch3.IQ4_NL.gguf	IQ4_NL	4.38GB
hp_ablations_llama3_epoch3.Q4_K_S.gguf	Q4_K_S	4.37GB
hp_ablations_llama3_epoch3.Q4_K.gguf	Q4_K	4.58GB
hp_ablations_llama3_epoch3.Q4_K_M.gguf	Q4_K_M	4.58GB
hp_ablations_llama3_epoch3.Q4_1.gguf	Q4_1	4.78GB
hp_ablations_llama3_epoch3.Q5_0.gguf	Q5_0	5.21GB
hp_ablations_llama3_epoch3.Q5_K_S.gguf	Q5_K_S	5.21GB
hp_ablations_llama3_epoch3.Q5_K.gguf	Q5_K	5.34GB
hp_ablations_llama3_epoch3.Q5_K_M.gguf	Q5_K_M	5.34GB
hp_ablations_llama3_epoch3.Q5_1.gguf	Q5_1	5.65GB
hp_ablations_llama3_epoch3.Q6_K.gguf	Q6_K	6.14GB
hp_ablations_llama3_epoch3.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 tags:

llama-factory
full
generated_from_trainer model-index:
name: hp_ablations_llama3_epoch3 results: []

hp_ablations_llama3_epoch3

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the mlfoundations-dev/oh-dcft-v3.1-gpt-4o-mini dataset. It achieves the following results on the evaluation set:

Loss: 0.6414

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: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 16
gradient_accumulation_steps: 4
total_train_batch_size: 512
total_eval_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.1
lr_scheduler_warmup_steps: 1738
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.6483	0.9982	422	0.6504
0.5968	1.9988	845	0.6400
0.572	2.9947	1266	0.6414

Framework versions

Transformers 4.46.1
Pytorch 2.3.0
Datasets 3.1.0
Tokenizers 0.20.3

RichardErkhov/mlfoundations-dev_-_hp_ablations_llama3_epoch3-gguf

Original model description:

hp_ablations_llama3_epoch3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
mlfoundations-dev_-_hp_ablations_llama3_epoch3-gguf