File size: 1,564 Bytes
5670be8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
---
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.modeling import DmxModel
from bigcode_eval.evaluator import Evaluator
pipe = pipeline(
task="text-generation",
model="d-matrix/CodeLlama-7b",
trust_remote_code=True,
)
# Transform the model with DMX
model = DmxModel.from_torch(pipe.model).to_basic_model() # Using BASIC configuration
model(torch.randint(1, 100, (1, max_length))) # Assign desired max length of generation
evaluator = Evaluator(accelerator, model, tokenizer, eval_args)
eval_results = evaluator.evaluate(task) # Assign desired task, i.e. "humaneval"
``` |