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RichardErkhov/
barc0_-_transduction_rearc_fft_35k-gguf

Name: barc0_-_transduction_rearc_fft_35k-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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transduction_rearc_fft_35k - GGUF

Model creator: https://huggingface.co/barc0/
Original model: https://huggingface.co/barc0/transduction_rearc_fft_35k/

Name	Quant method	Size
transduction_rearc_fft_35k.Q2_K.gguf	Q2_K	2.96GB
transduction_rearc_fft_35k.IQ3_XS.gguf	IQ3_XS	3.28GB
transduction_rearc_fft_35k.IQ3_S.gguf	IQ3_S	3.43GB
transduction_rearc_fft_35k.Q3_K_S.gguf	Q3_K_S	3.41GB
transduction_rearc_fft_35k.IQ3_M.gguf	IQ3_M	3.52GB
transduction_rearc_fft_35k.Q3_K.gguf	Q3_K	3.74GB
transduction_rearc_fft_35k.Q3_K_M.gguf	Q3_K_M	3.74GB
transduction_rearc_fft_35k.Q3_K_L.gguf	Q3_K_L	4.03GB
transduction_rearc_fft_35k.IQ4_XS.gguf	IQ4_XS	4.18GB
transduction_rearc_fft_35k.Q4_0.gguf	Q4_0	4.34GB
transduction_rearc_fft_35k.IQ4_NL.gguf	IQ4_NL	4.38GB
transduction_rearc_fft_35k.Q4_K_S.gguf	Q4_K_S	4.37GB
transduction_rearc_fft_35k.Q4_K.gguf	Q4_K	4.58GB
transduction_rearc_fft_35k.Q4_K_M.gguf	Q4_K_M	4.58GB
transduction_rearc_fft_35k.Q4_1.gguf	Q4_1	4.78GB
transduction_rearc_fft_35k.Q5_0.gguf	Q5_0	5.21GB
transduction_rearc_fft_35k.Q5_K_S.gguf	Q5_K_S	5.21GB
transduction_rearc_fft_35k.Q5_K.gguf	Q5_K	5.34GB
transduction_rearc_fft_35k.Q5_K_M.gguf	Q5_K_M	5.34GB
transduction_rearc_fft_35k.Q5_1.gguf	Q5_1	5.65GB
transduction_rearc_fft_35k.Q6_K.gguf	Q6_K	6.14GB
transduction_rearc_fft_35k.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:

alignment-handbook
generated_from_trainer datasets:
barc0/transduction_rearc model-index:
name: barc-llama3.1-8b-instruct-fft-sft-transduction_rearc_lr4e-5_epoch2 results: []

barc-llama3.1-8b-instruct-fft-sft-transduction_rearc_lr4e-5_epoch2

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the barc0/transduction_rearc dataset. It achieves the following results on the evaluation set:

Loss: 0.2794

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: 4e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.3971	0.9981	259	0.4259
0.2353	1.9961	518	0.2794

Framework versions

Transformers 4.45.0.dev0
Pytorch 2.4.0+cu121
Datasets 3.0.0
Tokenizers 0.19.1

RichardErkhov/barc0_-_transduction_rearc_fft_35k-gguf

Original model description:

barc-llama3.1-8b-instruct-fft-sft-transduction_rearc_lr4e-5_epoch2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
barc0_-_transduction_rearc_fft_35k-gguf