Update README.md

README.md

@@ -1,3 +1,117 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- linux
+- gpl2
+- mit
+---
+
+
+# LaaM - Linux as a Model
+What happens when we train a simple transformer model to memorize the GPL2 source of the Linux kernel?
+
+## Source
+[https://github.com/mjbommar/laam](https://github.com/mjbommar/laam)
+ 
+## Motivation
+Simply put, the OSI is making a grave mistake by ignoring the most important transitive dependency in AI - the training data.
+
+As of the latest version of [The Open Source AI Definition (draft v. 0.0.8)](https://opensource.org/deepdive/drafts/the-open-source-ai-definition-draft-v-0-0-8), 
+the OSI has decided that the legal status of training data is irrelevant to their subsequent "approval" of models as "open."
+
+The argument in favor of this omission is that such a requirement would be inconvenient and legally ambiguous
+in some jurisdictions. 
+
+This would be like Creative Commons encouraging the authors of textual or audiovisual works to ignore
+the terms of copyleft licenses.
+
+**Simply put, organizations like the OSI must take a clear, common sense stance: "AI" models like text or multimodal LLMs
+cannot be considered "open" if they are trained on "stolen" or "closed source" data.**    
+
+## Details
+To demonstrate how ridiculous the OSI's position is, I have trained simple transformer models to memorize the
+source code of Linux version 1.0, which is licensed under the GPL2.
+
+This model is documented and trained in perfect compliance with the OSI's draft guidance on Data Information, Code, 
+and Model sections.  All source code is available in the GitHub repository, all dependencies are open source,
+all input training data is directly described by the source code, and all model weights are available on 
+Hugging Face.
+
+## Example Model - 5M parameter Llama2 architecture
+For example, this 5M parameter model can be trained on practically any device in a minutes to hours.  The model trivially 
+emits copies of Linux 1.0 source code.  For example, using the HuggingFace hub copy at `mjbommar/linux-as-a-model-5M`:
+
+```python
+>>> from transformers import pipeline
+>>> p = pipeline('text-generation', 'mjbommar/linux-as-a-model-5M')
+>>> print(p('', max_new_tokens=256, do_sample=True, temperature=0.2)[0]['generated_text'])
+ linux/drivers/net/3c503.c /* 3c503.c: A shared-memory NS8390 ethernet driver for linux. */
+/*
+ Written 1992,1993 by Donald Becker.
+
+ Copyright 1993 United States Government as represented by the
+ Director, National Security Agency. This software may be used and
+ distributed according to the terms of the GNU Public License,
+ incorporated herein by reference.
+
+ This driver should work with the 3c503 and 3c503/16. It should be used
+ in shared memory mode for best performance, although it may also work
+ in programmed-I/O mode.
+
+ The Author may be reached as becker@super.org or
+ C/O Supercomputing Research Ctr., 17100 Science Dr., Bowie MD 20715
+*/
+
+```
+
+## License
+For the sake of demonstration, I have licensed the model source **and weights** under the MIT terms,
+and the OSI should support this model as completely open and compliant with their draft guidance.
+
+
+## Train your own model
+```
+# ensure poetry available
+# curl -sSL https://install.python-poetry.org | python3 -
+
+# setup poetry environment
+$ poetry install --no-root
+
+# optionally install flash-attn
+# poetry run pip install wheel
+# MAX_JOBS=4 poetry run pip install flash-attn --no-build-isolation
+
+# train a tokenizer with fixed vocab size on linux version 1.0
+$ PYTHONPATH=. poetry run python3 -m laam.commands.train_tokenizer \
+    --version v1.0/1.0 \
+    --vocab-size 32768
+
+# train a 5M parameter model on it
+
+# stage 1: large batch size, 1e-3 learning rate to safely converge near solution 
+$ PYTHONPATH=. poetry run accelerate launch \
+    laam/commands/train_llama.py \
+    --version v1.0/1.0 \
+    --precision bfloat16 \
+    --hidden_size 64 \
+    --intermediate_size 256 \
+    --num_hidden_layers 8 \
+    --num_attention_heads 32 \
+    --max_position_embeddings 512 \
+    --learning_rate 0.001 \
+    --batch_size 64 \
+    --epochs 100
+    
+# stage 2: single sample batches with 1e-4 learning rate to memorize
+$ PYTHONPATH=. poetry run accelerate launch \
+    laam/commands/train_llama.py \
+    --version v1.0/1.0 \
+    --precision bfloat16 \
+    --reload \
+    --learning_rate 0.0001 \
+    --batch_size 1 \
+    --epochs 100
+```