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README.md

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----
-model-index:
-- name: opt-125m
-  results:
-  - task:
-      type: text-generation
-    dataset:
-      name: Wikitext
-      type: wikitext
-    metrics:
-    - type: perplexity (BASELINE)
-      value: 31.94644314710864
-    - type: perplexity (BASIC)
-      value: 32.05778110592746
----
-This is a d-Matrix functional reference of the OPT-125M 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 `BFP16-64`, and all other operations transformed to approximated kernel simulations
-
-
-### Usage
-
-Install d-Matrix [Dmx_Compressor](https://github.com/d-matrix-ai/dmx-compressor) first.
-```sh
-pip install dmx_compressor
-```
-
-The following is an example model and its evaluation.
-
-```sh
-pip install lm-eval
-```
-
-```python
-from dmx.compressor import config_rules
-from dmx.compressor.modeling import DmxModel
-import lm_eval
-
-model_args = f"pretrained="d-matrix/opt-125m",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).to_basic_model()  # Using BASIC configuration
-
-evaluation_results = lm_eval.evaluate(lm, lm_eval.tasks.get_task_dict([task_name])  # Assign desired task to task_name, i.e. "wikitext"
-```