Transformer + GFM
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Nanjing University
- Organisation type
- Academia
- Country
- China
- Published
- 1 December 2022
- Authors
- Hao Yu, Jianxin Wu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Language modeling
- Base model
- FAIRSEQ Adaptive Inputs
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 185.2M
- Training data
- 103,000,000 tokens
185.2M (Table 4) "We implemented our methods based on fairseq (Ott et al. 2019). The original transformer model follows the architectural choice described in Baevski and Auli (2018), which includes 16 decoder blocks and sinusoidal position embeddings in the input layer. Each MHSA module has 8 heads and adaptive input representations have three bands of size 20K, 40K and 200K. The embedding layer and FFN’s hidden-state have dimensions of 1024 and 4096, respectively. We sampled 4K sentences to fo…
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 7.7 × 10¹⁸ FLOP
- How it was established
- Operation counting,Hardware
- Fine-tuning compute
- 4.4 × 10¹⁷ FLOP
7.30e18 FLOP [base transformer] + 4.3694162e+17 FLOP [GFM] = 7.7369416e+18 FLOP _______________ Estimations from the Algorithmic progress paper (upd - a100 gpu were assumed while the paper reports 3090 gpus): SOURCE: Compression of Baevski et al. transformer, impute During the GFM process, we removed layer dropout and trained on 8 GPUs. We limited the number of tokens per GPU to a maximum threshold 1536, which means each GPU processes 1536 tokens using the same model parameters. We accumulat…
6 FLOP/token/parameter * 185000000 parameters * 103000000 tokens = 1.1433e+17 FLOP or 0.6 hours - reported training time for another model in the paper, I assumed, this training was similar in time length 0.6 hours * 3600 sec / hour * 8 GPUs * 35580000000000 FLOP/s [assumed precision fp16] * 0.3 [assumed utilization] = 1.8444672e+17 FLOP or "During the GFM process, we removed layer dropout and trained on 8 GPUs. We limited the number of tokens per GPU to a maximum threshold 1536, which me…
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce RTX 3090
- Chips used
- 8
- Chip-hours
- 1
- Power draw
- 5.6 kW
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Benchmark data
- Transformer + GFM
Sources
Where this record came from and when it was last checked.
- Reference
- "Compressing Transformers: Features Are Low-Rank, but Weights Are Not"
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Transformer + GFM was published by Nanjing University, in China, in December 2022. academia is the category the publisher falls under.
It works in Language, and is recorded as doing language modeling.
It is derived from FAIRSEQ Adaptive Inputs rather than trained from scratch, which is the usual way a specialised model is produced.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
Producing it required around 7.7 × 10¹⁸ FLOP of arithmetic, on NVIDIA GeForce RTX 3090, which is a statement about the training budget rather than about inference.
It was trained on about 103,000,000 tokens of text.
Answers
Transformer + GFM — common questions
What is Transformer + GFM used for?
Transformer + GFM works in Language, and is recorded as handling language modeling. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
How much compute was used to train Transformer + GFM?
Around 7.7 × 10¹⁸ FLOP, on NVIDIA GeForce RTX 3090. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run Transformer + GFM?
None. Transformer + GFM is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Transformer + GFM open source?
No. Transformer + GFM has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Transformer + GFM have?
Transformer + GFM has 185.2M parameters. 185.2M (Table 4) "We implemented our methods based on fairseq (Ott et al. 2019). The original transformer model follows the architectural choice described in Baevski and Auli (2018), which includes 16 decoder blocks and sinusoidal position embeddings in the input layer. Each MHSA module has 8 heads and adaptive input representations have three bands of size 20K, 40K and 200K. The embedding layer and FFN’s hidden-state have dimensions of 1024 and 4096, respectively. We sampled 4K sentences to form the proxy dataset D. Then, we reduced the parameters by 15%, 20% and 25%.". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created Transformer + GFM?
Transformer + GFM was published by Nanjing University, based in China, categorised as academia.
When was Transformer + GFM released?
Transformer + GFM was published in December 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.