Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,55 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
base_model: neuralmagic/Llama-2-7b-pruned70-retrained-evolcodealpaca
|
3 |
+
inference: false
|
4 |
+
model_type: llama
|
5 |
+
pipeline_tag: text-generation
|
6 |
+
datasets:
|
7 |
+
- cerebras/SlimPajama-627B
|
8 |
+
- theblackcat102/evol-codealpaca-v1
|
9 |
+
tags:
|
10 |
+
- sparse
|
11 |
+
- code
|
12 |
+
- deepsparse
|
13 |
+
---
|
14 |
+
|
15 |
+
# Llama-2-7b-pruned70-retrained-evolcodealpaca-quant-ds
|
16 |
+
|
17 |
+
This repo contains a [70% sparse Llama 2 7B](https://huggingface.co/neuralmagic/Llama-2-7b-pruned70-retrained) finetuned for code generation tasks using the [Evolved CodeAlpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset.
|
18 |
+
It was then quantized to 8-bit weights + activations and exported to deploy with [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
|
19 |
+
|
20 |
+
**Authors**: Neural Magic, Cerebras
|
21 |
+
|
22 |
+
## Usage
|
23 |
+
|
24 |
+
Below we share some code snippets on how to get quickly started with running the model.
|
25 |
+
|
26 |
+
### Sparse Transfer
|
27 |
+
|
28 |
+
By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process [here](https://neuralmagic.github.io/docs-v2/get-started/transfer).
|
29 |
+
|
30 |
+
### Running the model
|
31 |
+
|
32 |
+
For accelerated inference with sparsity on CPUs, deploy with [deepsparse](https://github.com/neuralmagic/deepsparse).
|
33 |
+
|
34 |
+
```python
|
35 |
+
# pip install deepsparse[llm]
|
36 |
+
from deepsparse import TextGeneration
|
37 |
+
|
38 |
+
model = TextGeneration(model_path="hf:neuralmagic/Llama-2-7b-pruned70-retrained-evolcodealpaca-quant-ds")
|
39 |
+
|
40 |
+
input_text = "def fibonacci(n):\n"
|
41 |
+
outputs = model(input_text, max_new_tokens=100)
|
42 |
+
print(outputs.generations[0].text)
|
43 |
+
```
|
44 |
+
|
45 |
+
## Evaluation Benchmark Results
|
46 |
+
|
47 |
+
Model evaluation metrics and results.
|
48 |
+
|
49 |
+
| Benchmark | Metric | Llama-2-7b-instruct | Llama-2-7b-pruned70-retrained-evolcodealpaca-quant-ds |
|
50 |
+
|------------------------------------------------|---------------|-------------|-------------------------------|
|
51 |
+
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | xxxx | xxxx |
|
52 |
+
|
53 |
+
## Help
|
54 |
+
|
55 |
+
For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)
|