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yonas_-_SmolLM-135M-Instruct-grammarly-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Quantization made by Richard Erkhov.
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SmolLM-135M-Instruct-grammarly - GGUF
Model creator:
https://huggingface.co/yonas/
Original model:
https://huggingface.co/yonas/SmolLM-135M-Instruct-grammarly/
Name
Quant method
Size
SmolLM-135M-Instruct-grammarly.Q2_K.gguf
Q2_K
0.08GB
SmolLM-135M-Instruct-grammarly.IQ3_XS.gguf
IQ3_XS
0.08GB
SmolLM-135M-Instruct-grammarly.IQ3_S.gguf
IQ3_S
0.08GB
SmolLM-135M-Instruct-grammarly.Q3_K_S.gguf
Q3_K_S
0.08GB
SmolLM-135M-Instruct-grammarly.IQ3_M.gguf
IQ3_M
0.08GB
SmolLM-135M-Instruct-grammarly.Q3_K.gguf
Q3_K
0.09GB
SmolLM-135M-Instruct-grammarly.Q3_K_M.gguf
Q3_K_M
0.09GB
SmolLM-135M-Instruct-grammarly.Q3_K_L.gguf
Q3_K_L
0.09GB
SmolLM-135M-Instruct-grammarly.IQ4_XS.gguf
IQ4_XS
0.09GB
SmolLM-135M-Instruct-grammarly.Q4_0.gguf
Q4_0
0.09GB
SmolLM-135M-Instruct-grammarly.IQ4_NL.gguf
IQ4_NL
0.09GB
SmolLM-135M-Instruct-grammarly.Q4_K_S.gguf
Q4_K_S
0.1GB
SmolLM-135M-Instruct-grammarly.Q4_K.gguf
Q4_K
0.1GB
SmolLM-135M-Instruct-grammarly.Q4_K_M.gguf
Q4_K_M
0.1GB
SmolLM-135M-Instruct-grammarly.Q4_1.gguf
Q4_1
0.09GB
SmolLM-135M-Instruct-grammarly.Q5_0.gguf
Q5_0
0.1GB
SmolLM-135M-Instruct-grammarly.Q5_K_S.gguf
Q5_K_S
0.1GB
SmolLM-135M-Instruct-grammarly.Q5_K.gguf
Q5_K
0.1GB
SmolLM-135M-Instruct-grammarly.Q5_K_M.gguf
Q5_K_M
0.1GB
SmolLM-135M-Instruct-grammarly.Q5_1.gguf
Q5_1
0.1GB
SmolLM-135M-Instruct-grammarly.Q6_K.gguf
Q6_K
0.13GB
SmolLM-135M-Instruct-grammarly.Q8_0.gguf
Q8_0
0.13GB
Original model description:
library_name: transformers license: apache-2.0 base_model: HuggingFaceTB/SmolLM-135M-Instruct tags:
trl
sft
generated_from_trainer model-index:
name: SmolLM-135M-Instruct_finetuned results: []
SmolLM-135M-Instruct_finetuned
This model is a fine-tuned version of
HuggingFaceTB/SmolLM-135M-Instruct
on an unknown dataset.
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: 3e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 2
Training results
Framework versions
Transformers 4.45.2
Pytorch 2.0.1
Datasets 3.0.1
Tokenizers 0.20.1
