gpjt
/

8xa100m40

@@ -1,199 +1,86 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+  - gpjt-llm-from-scratch
+datasets:
+  - gpjt/fineweb-gpt2-tokens
 ---
+# Model Card for gpjt/8xa100m40
+This model is gpjt/8xa100m40, a trained-from-scratch base model using
+the GPT-2-style architecture from [Sebastian Raschka](https://sebastianraschka.com/)'s book
+"[Build a Large Language Model (from Scratch)](https://www.manning.com/books/build-a-large-language-model-from-scratch)".
 ## Model Details
 ### Model Description
+- **Developed by:** [Giles Thomas](https://huggingface.co/gpjt), based on code by [Sebastian Raschka](https://huggingface.co/rasbt)
+- **Model type:** GPT-2 style transformers-based causal LLM.
+- **License:** [Apache 2](https://huggingface.co/models?license=license:apache-2.0&sort=downloads)
+- **Parameters:** 163,009,536
+- **Context length:** 1,024
+- **Embedding dimensions:** 768
+- **MHA heads:** 12
+- **Layers:** 12
+- **QKV bias:** False
+- **Weight tying:** No.
+Don't have high expectations for the model!  It has only 163M parameters (the GPT-2 "small" size)
+and was trained on roughly the Chinchilla-optimal number of tokens (~20x the number of parameters), which means that it doesn't know
+many facts and is not terribly smart.  If you want to do serious work, use a serious model (I like
+[Qwen's](https://huggingface.co/Qwen)).  But if you want to build on this and see what you can do with a 2020-vintage
+LLM, please do feel free to play with it!
+### Model Sources
+- **Repository:** [gpjt/ddp-base-model-from-scratch](https://github.com/gpjt/ddp-base-model-from-scratch)
+- **Blog post:** [Writing an LLM from scratch, part 29 -- using DistributedDataParallel to train a base model from scratch in the cloud](https://www.gilesthomas.com/2026/01/llm-from-scratch-29-ddp-training-a-base-model-in-the-cloud)
 ## How to Get Started with the Model
+You can download and run the model for inference directly:
+```python
+from transformers import pipeline
+pipe = pipeline("text-generation", model="gpjt/8xa100m40", trust_remote_code=True)
+out = pipe(
+    "Every effort moves you",
+    max_new_tokens=20,
+    do_sample=True,
+    temperature=1.4,
+    top_k=25,
+)
+print(out[0]["generated_text"])
+```
+Note that because it uses custom code, you'll need to set `trust_remote_code` to `True`.
+It supports `AutoTokenizer`, `AutoModel` and `AutoModelForCausalLM`:
+```python
+>>> from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
+>>> tokenizer = AutoTokenizer.from_pretrained("gpjt/8xa100m40")
+>>> model = AutoModel.from_pretrained("gpjt/8xa100m40", trust_remote_code=True)
+>>> llm_model = AutoModelForCausalLM.from_pretrained("gpjt/8xa100m40", trust_remote_code=True)
+```
+You can also fine-tune it; [this notebook](https://github.com/gpjt/ddp-base-model-from-scratch/blob/main/hf_train.ipynb) has an example.
+Again, don't expect too much from this model!  It's a 163M-parameter GPT-2 one, trained on a limited
+number of tokens.  It's [both dumb and ignorant](https://www.gilesthomas.com/2026/01/llm-from-scratch-30-digging-into-llm-as-a-judge) ;-)
+## Training Details
+- **Machine type:** TODO
+- **Tokens:**  3,260,190,720 (Chinchilla-optimal of 20x parameters) rounded up to the nearest batch.
+- **Dataset:** [gpjt/fineweb-gpt2-tokens](https://huggingface.co/datasets/gpjt/fineweb-gpt2-tokens)
+- **Micro-batch size:** 13
+- **Global batch size:** TODO
+- **Dropout:** 0.1