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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
+```
+
+The following is an example model and its evaluation.
+
+```sh
+git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
+cd bigcode-evaluation-harness
+pip install .
+```
+
+```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)
+```