CODEFUSION (Python)
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
- Microsoft,Microsoft Research
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 26 October 2023
- Authors
- Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, Gust Verbruggen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Code generation
- Approach
- Self-supervised learning
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
- 75M
- Training data
- 4,390,400 tokens
Table 1
Section A3, Table 5: for python, 56k samples with an average length of 78.4 tokens
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
- 7.9 × 10¹⁸ FLOP
- How it was established
- Hardware
V100 performance: 125 teraFLOPS according to https://www.nvidia.com/en-us/data-center/v100/ 11 hours * 4 GPUs * 125 teraFLOPS/GPU * 0.40 utilization = 7.92e18 FLOP
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 V100
- Chips used
- 4
- Wall-clock time
- 11 hours
- Power draw
- 2.4 kW
- Compute cost
- $9
"The system used to run the experiments uses an Intel Core i7 processor (base at 1.8 GHz) along with 4 V100 GPU units, a 64-bit operating system, and 56 GB RAM. CODEFUSION took 8 hours to pre-train and 3 hours to fine-tune on average for each dataset."
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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Confident
- Citations
- 57
See Table 1, SOTA in Python code generation "We evaluate Python using CodeBERTScore (Zhou et al., 2023), which has been shown to be a high quality non-execution-based code matching metric."
Sources
Where this record came from and when it was last checked.
- Reference
- CODEFUSION: A Pre-trained Diffusion Model for Code Generation
- Last updated
- 25 May 2026
What the numbers mean
About this model
CODEFUSION (Python) was published by Microsoft,Microsoft Research, in United States of America, in October 2023. The organisation is categorised as industry,Industry.
It works in Language, and is recorded as doing code generation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training it took roughly 7.9 × 10¹⁸ FLOP of computation, on NVIDIA V100 — a measure of what producing the model cost, not of how fast it answers.
The training set ran to roughly 4,390,400 tokens.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
CODEFUSION (Python) — common questions
How much compute was used to train CODEFUSION (Python)?
Around 7.9 × 10¹⁸ FLOP, on NVIDIA V100. 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 CODEFUSION (Python)?
None. CODEFUSION (Python) 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 CODEFUSION (Python) open source?
No. CODEFUSION (Python) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does CODEFUSION (Python) have?
CODEFUSION (Python) has 75M parameters. Table 1. 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 CODEFUSION (Python)?
CODEFUSION (Python) was published by Microsoft,Microsoft Research, based in United States of America, categorised as industry,Industry.
When was CODEFUSION (Python) released?
CODEFUSION (Python) was published in October 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is CODEFUSION (Python) used for?
CODEFUSION (Python) works in Language, and is recorded as handling code generation. These are the areas it was designed around; they describe intent rather than a hard boundary.
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.