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README.md

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----
-license: apache-2.0
-datasets:
-- wikitext
-- ptb_text_only
-language:
-- en
-metrics:
-- perplexity
-pipeline_tag: text-generation
-model-index:
-- name: distilgpt2
-  results:
-  - task:
-      type: text-generation
-    dataset:
-      name: penn_treebank
-      type: ptb_text_only
-    metrics:
-    - name: perlexity@BASELINE
-      type: dmx-perlexity
-      value: 63.45857238769531
-    - name: perlexity@FALLBACK
-      type: dmx-perlexity
-      value: 64.36720275878906
-  - task:
-      type: text-generation
-    dataset:
-      name: wikitext2
-      type: wikitext-2-raw-v1
-    metrics:
-    - name: perlexity@BASELINE
-      type: dmx-perlexity
-      value: 46.05925369262695
-    - name: perlexity@FALLBACK
-      type: dmx-perlexity
-      value: 46.570838928222656
----
-This is a d-Matrix functional reference of the GPT2 model family, of the following *revisions*: 
-- [`distilgpt2`](https://huggingface.co/distilbert/distilgpt2)
-- [`gpt2`](https://huggingface.co/openai-community/gpt2)
-- [`gpt2-medium`](https://huggingface.co/openai-community/gpt2-medium) 
-- [`gpt2-large`](https://huggingface.co/openai-community/gpt2-large) 
-- [`gpt2-xl`](https://huggingface.co/openai-community/gpt2-xl)
-
-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
-
-Prerequisites:
-- Install d-Matrix ML Tools: `pip install dmx-mltools`
-- clone this repo. `cd` to the cloned repo.
-
-```python
->>> import os
->>> from mltools import dmx
->>> from transformers import pipeline
->>> import evaluate
->>> from datasets import load_dataset
-
->>> pipe = pipeline(
->>>     "text-generation",
->>>     model="d-matrix/gpt2",
->>>     revision="gpt2-xl",
->>>     dmx_config="BASELINE",
->>>     use_auth_token=os.environ.get("HUGGING_FACE_HUB_TOKEN"),
->>>     trust_remote_code=True,
->>>     # device_map="auto",  # enabling model parallel on multi-GPU nodes
->>> )
->>> pipe.model = dmx.Model(
->>>     pipe.model, monkey_patched=False, hf=True, input_names=["input_ids", "labels"]
->>> )
-
->>> perplexity = evaluate.load("d-matrix/dmx_perplexity", module_type="metric")
->>> input_texts = load_dataset("ptb_text_only", "penn_treebank", split="test")["sentence"]
->>> results = perplexity.compute(model=pipe.model.body, references=input_texts)
->>> print(results)
-{'loss': 3.0451672077178955, 'perplexity': 21.0135440826416}
-```
-
-### Evaluation results