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RichardErkhov/
ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf

Name: ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf
Availability: InStock

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SELM-Llama-3-8B-Instruct-iter-1 - GGUF

Model creator: https://huggingface.co/ZhangShenao/
Original model: https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-1/

Name	Quant method	Size
SELM-Llama-3-8B-Instruct-iter-1.Q2_K.gguf	Q2_K	2.96GB
SELM-Llama-3-8B-Instruct-iter-1.IQ3_XS.gguf	IQ3_XS	3.28GB
SELM-Llama-3-8B-Instruct-iter-1.IQ3_S.gguf	IQ3_S	3.43GB
SELM-Llama-3-8B-Instruct-iter-1.Q3_K_S.gguf	Q3_K_S	3.41GB
SELM-Llama-3-8B-Instruct-iter-1.IQ3_M.gguf	IQ3_M	3.52GB
SELM-Llama-3-8B-Instruct-iter-1.Q3_K.gguf	Q3_K	3.74GB
SELM-Llama-3-8B-Instruct-iter-1.Q3_K_M.gguf	Q3_K_M	3.74GB
SELM-Llama-3-8B-Instruct-iter-1.Q3_K_L.gguf	Q3_K_L	4.03GB
SELM-Llama-3-8B-Instruct-iter-1.IQ4_XS.gguf	IQ4_XS	4.18GB
SELM-Llama-3-8B-Instruct-iter-1.Q4_0.gguf	Q4_0	4.34GB
SELM-Llama-3-8B-Instruct-iter-1.IQ4_NL.gguf	IQ4_NL	4.38GB
SELM-Llama-3-8B-Instruct-iter-1.Q4_K_S.gguf	Q4_K_S	4.37GB
SELM-Llama-3-8B-Instruct-iter-1.Q4_K.gguf	Q4_K	4.58GB
SELM-Llama-3-8B-Instruct-iter-1.Q4_K_M.gguf	Q4_K_M	4.58GB
SELM-Llama-3-8B-Instruct-iter-1.Q4_1.gguf	Q4_1	4.78GB
SELM-Llama-3-8B-Instruct-iter-1.Q5_0.gguf	Q5_0	5.21GB
SELM-Llama-3-8B-Instruct-iter-1.Q5_K_S.gguf	Q5_K_S	5.21GB
SELM-Llama-3-8B-Instruct-iter-1.Q5_K.gguf	Q5_K	5.34GB
SELM-Llama-3-8B-Instruct-iter-1.Q5_K_M.gguf	Q5_K_M	5.34GB
SELM-Llama-3-8B-Instruct-iter-1.Q5_1.gguf	Q5_1	5.65GB
SELM-Llama-3-8B-Instruct-iter-1.Q6_K.gguf	Q6_K	6.14GB
SELM-Llama-3-8B-Instruct-iter-1.Q8_0.gguf	Q8_0	7.95GB

Original model description:

license: mit base_model: meta-llama/Meta-Llama-3-8B-Instruct tags:

alignment-handbook
dpo
trl
selm datasets:
HuggingFaceH4/ultrafeedback_binarized model-index:
name: SELM-Llama-3-8B-Instruct-iter-1 results: []

Self-Exploring Language Models: Active Preference Elicitation for Online Alignment.

SELM-Llama-3-8B-Instruct-iter-1

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct using synthetic data based on on the HuggingFaceH4/ultrafeedback_binarized dataset.

Model description

Model type: A 8B parameter Llama3-instruct-based Self-Exploring Language Models (SELM).
License: MIT

Results

	AlpacaEval 2.0 (LC WR)	MT-Bench (Average)
SELM-Llama-3-8B-Instruct-iter-3	33.47	8.29
SELM-Llama-3-8B-Instruct-iter-2	35.65	8.09
SELM-Llama-3-8B-Instruct-iter-1	32.02	7.92
Meta-Llama-3-8B-Instruct	24.31	7.93

Training hyperparameters

The following hyperparameters were used during training:

alpha: 0.0001
beta: 0.01
train_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
num_epochs: 1

Framework versions

Transformers 4.40.2
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.19.1

RichardErkhov/ZhangShenao_-_SELM-Llama-3-8B-Instruct-iter-1-gguf