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gpt2-large
d-matrix/gpt2-large
gpt2-large is a machine learning model from d-matrix. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This is a d-Matrix functional reference of the GPT2-LARGE model. The reference provides the following functional configurations: Configuration | Explanation :-- | :-- BASELINE | a reference functionally equivalent to…
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At a glance
- Model type
- gpt2
- Access
- Public
- Created
- Nov 10, 2024
- Updated
- Jul 25, 2025
- SHA
- 8dc2ad56
Model card
From the Hugging Face model README
This is a d-Matrix functional reference of the GPT2-LARGE model. The reference provides the following functional configurations:
| Configuration | Explanation |
|---|---|
BASELINE | a reference functionally equivalent to the original model |
BASIC | all linear algebraic operands quantized to MXINT8-64 |
Usage
Install d-Matrix Dmx_Compressor first.
pip install dmx_compressor
The following is an example model and its evaluation.
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
from dmx.compressor.modeling import DmxModel
import lm_eval
from lm_eval.models.huggingface import HFLM
lm_eval.api.registry.register_model("hf", HFLM)
model_args = "pretrained=d-matrix/gpt2-large,trust_remote_code=True"
lm = lm_eval.api.registry.get_model("hf").create_from_arg_string(model_args, {"batch_size": 1})
# Transform the model with DMX
lm._model = DmxModel.from_torch(lm._model)
eval_results = lm_eval.evaluate(lm, lm_eval.tasks.get_task_dict(["wikitext"])) # Assign desired task, i.e. "wikitext"
- Type
- gpt2
- Created
- Nov 10, 2024
- Updated
- Jul 25, 2025