Delete README.md

README.md

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----
-model-index:
-- name: CodeLlama-7b
-  results:
-  - task:
-      type: code-generation
-    dataset:
-      name: Humaneval
-      type: humaneval
-    metrics:
-    - type: pass@1 (BASELINE)
-      value: 0.3048780487804878
-    - type: pass@1 (BASIC)
-      value: 0.3170731707317073
----
-This is a d-Matrix functional reference of the CODELLAMA-7B 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
-
-git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
-cd bigcode-evaluation-harness
-pip install .
-```
-
-The following is an example model and its evaluation.
-
-```python
-from dmx.compressor import config_rules
-from dmx.compressor.modeling import DmxModel
-from bigcode_eval.evaluator import Evaluator
-
-pipe = pipeline(
-  task="text-generation",
-  model="d-matrix/d-matrix/CodeLlama-7b",
-  trust_remote_code=True,
-  device=0,
-)
-
-# Transform the model with DMX
-model = DmxModel.from_torch(pipe.model).to("cuda")
-tensor = torch.randint(1, 100, (1, max_length)).to("cuda")  # Assign desired max length of generation to max_length
-model.transform(model.dmx_config, *eval(f"config_rules.{dmx_config}"))
-setup = model(tensor)
-
-evaluator = Evaluator(accelerator, model, tokenizer, eval_args)
-
-result = evaluator.evaluate(task)
-```