idea: Precision scaling research

[hahuyhoang411]

Problem Statement

Hypothesis: Increasing numerical precision during training can improve the performance of small language models (≈1B parameters), potentially enabling them to achieve capabilities comparable to larger models (3B-7B parameters).

Implications

If validated, this hypothesis could:

• Reduce the computational resources needed for training effective language models
• Enable broader adoption of smaller, more efficient models
• Lead to new approaches in optimizer design and implementation

Idea

Reference: https://arxiv.org/pdf/2411.04330

[hahuyhoang411]

@bachvudinh as co-author please help me to add some exploded attempts and MMLU score

We have ran some testing to train Llama 3.2 1B Instruct to check if fp32 can perform better bf16

Precision	Learning Rate	Weight Decay	Global batchsize	Trained Samples	Final Loss	MMLU
fp32	3e-4	0.01	96	0.2M	1.24
fp32	2.5e-4	0.01	96	0.2M	1.22
bf16	3e-4	0.01	96	0.2M	exploded
bf16	2e-4	0.01	96	0.2M	exploded
b16	2.5e-4	0.01	96	0.2M	1.26
fp32	3e-4	0.2	?	0.5M	0.67	25.54
fp32	1e-4	0.05	?	1.7M	1.32	23.18

for the training configs with fp32 and setting lr as 1e-4 and weight decay as 0.05 , there are some weird mmlu results with checkpoint step 1000, 2000 and 3000:

• step 1000:
  

• step 2000:
  

• step 3000:
  

[hahuyhoang411]

fp32 3e-4

[hahuyhoang411]

fp32 2.5e-4

[hahuyhoang411]

bf16 2.5e-4

[hahuyhoang411]

fp32 0.5M

[hahuyhoang411]

fp32 1.7M

[tikikun]

A few pending issues:

• It's very obvious that even tho more stabilized the training is not converging and still have hiccup, which indicating issue inside the optimizer itself not being able to give direction good enough for the optimizing process
• Since we are currently hitting a wall on the optimizer itself, we will not continue scaling up the precision

Next steps:

• @tikikun to do his own research on optimizer
• We make use of the cluster for training qwen 32b instruct

cc @0xSage for interested