sruthigorantla_-_Mistral-7B-v0.1-math-sft-gguf – AI Model by RichardErkhov | AlphaNeural AI

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sruthigorantla_-_Mistral-7B-v0.1-math-sft-gguf

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Quantization made by Richard Erkhov.

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Mistral-7B-v0.1-math-sft - GGUF

Model creator: https://huggingface.co/sruthigorantla/
Original model: https://huggingface.co/sruthigorantla/Mistral-7B-v0.1-math-sft/

Name	Quant method	Size
Mistral-7B-v0.1-math-sft.Q2_K.gguf	Q2_K	2.53GB
Mistral-7B-v0.1-math-sft.IQ3_XS.gguf	IQ3_XS	2.81GB
Mistral-7B-v0.1-math-sft.IQ3_S.gguf	IQ3_S	2.96GB
Mistral-7B-v0.1-math-sft.Q3_K_S.gguf	Q3_K_S	2.95GB
Mistral-7B-v0.1-math-sft.IQ3_M.gguf	IQ3_M	3.06GB
Mistral-7B-v0.1-math-sft.Q3_K.gguf	Q3_K	3.28GB
Mistral-7B-v0.1-math-sft.Q3_K_M.gguf	Q3_K_M	3.28GB
Mistral-7B-v0.1-math-sft.Q3_K_L.gguf	Q3_K_L	3.56GB
Mistral-7B-v0.1-math-sft.IQ4_XS.gguf	IQ4_XS	3.67GB
Mistral-7B-v0.1-math-sft.Q4_0.gguf	Q4_0	3.83GB
Mistral-7B-v0.1-math-sft.IQ4_NL.gguf	IQ4_NL	3.87GB
Mistral-7B-v0.1-math-sft.Q4_K_S.gguf	Q4_K_S	3.86GB
Mistral-7B-v0.1-math-sft.Q4_K.gguf	Q4_K	4.07GB
Mistral-7B-v0.1-math-sft.Q4_K_M.gguf	Q4_K_M	4.07GB
Mistral-7B-v0.1-math-sft.Q4_1.gguf	Q4_1	4.24GB
Mistral-7B-v0.1-math-sft.Q5_0.gguf	Q5_0	4.65GB
Mistral-7B-v0.1-math-sft.Q5_K_S.gguf	Q5_K_S	4.65GB
Mistral-7B-v0.1-math-sft.Q5_K.gguf	Q5_K	4.78GB
Mistral-7B-v0.1-math-sft.Q5_K_M.gguf	Q5_K_M	4.78GB
Mistral-7B-v0.1-math-sft.Q5_1.gguf	Q5_1	5.07GB
Mistral-7B-v0.1-math-sft.Q6_K.gguf	Q6_K	5.53GB
Mistral-7B-v0.1-math-sft.Q8_0.gguf	Q8_0	7.17GB

Original model description:

library_name: transformers license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags:

trl
sft
generated_from_trainer datasets:
generator model-index:
name: Mistral-7B-v0.1-math-sft results: []

Mistral-7B-v0.1-math-sft

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.1278

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: 2e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 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: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.1711	1.0	396	0.1278

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1