alexmarques commited on
Commit
66616db
·
verified ·
1 Parent(s): 6f93c8f

Create README.md

Browse files
Files changed (1) hide show
  1. README.md +66 -0
README.md ADDED
@@ -0,0 +1,66 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ tags:
3
+ - vllm
4
+ - sparsity
5
+ - quantization
6
+ - int4
7
+ pipeline_tag: text-generation
8
+ license: llama3.1
9
+ base_model: neuralmagic/Sparse-Llama-3.1-8B-evolcodealpaca-2of4
10
+ datasets:
11
+ - theblackcat102/evol-codealpaca-v1
12
+ language:
13
+ - en
14
+ ---
15
+
16
+ # Sparse-Llama-3.1-8B-evolcodealpaca-2of4
17
+
18
+ ## Model Overview
19
+ - **Model Architecture:** Llama-3.1-8B
20
+ - **Input:** Text
21
+ - **Output:** Text
22
+ - **Model Optimizations:**
23
+ - **Sparsity:** 2:4
24
+ - **Release Date:** 11/21/2024
25
+ - **Version:** 1.0
26
+ - **License(s):** [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE)
27
+ - **Model Developers:** Neural Magic
28
+
29
+ This is a code completion AI model obtained by fine-tuning the 2:4 sparse [Sparse-Llama-3.1-8B-2of4](https://huggingface.co/neuralmagic/Sparse-Llama-3.1-8B-2of4) on the [evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset, followed by quantization
30
+ On the [HumanEval](https://arxiv.org/abs/2107.03374) benchmark, it achieves a pass@1 of 49.1, compared to 48.5 for the fine-tuned dense model [Llama-3.1-8B-evolcodealpaca](https://huggingface.co/neuralmagic/Llama-3.1-8B-evolcodealpaca) — demonstrating over **100% accuracy recovery**.
31
+
32
+
33
+ ### Model Optimizations
34
+
35
+ This inherits the optimizations from its parent, [Sparse-Llama-3.1-8B-2of4](https://huggingface.co/neuralmagic/Sparse-Llama-3.1-8B-2of4).
36
+ Namely, all linear operators within transformer blocks were pruned to the 2:4 sparsity pattern: in each group of four weights, two are retained while two are pruned.
37
+
38
+
39
+ ## Deployment with vLLM
40
+
41
+ This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend. vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
42
+
43
+
44
+ ## Evaluation
45
+
46
+ This model was evaluated on Neural Magic's fork of [EvalPlus](https://github.com/neuralmagic/evalplus).
47
+
48
+ ### Accuracy
49
+ #### Human Benchmark
50
+ <table>
51
+ <tr>
52
+ <td><strong>Metric</strong></td>
53
+ <td style="text-align: center"><strong>Llama-3.1-8B-evolcodealpaca</strong></td>
54
+ <td style="text-align: center"><strong>Sparse-Llama-3.1-8B-evolcodealpaca-2of4</strong></td>
55
+ </tr>
56
+ <tr>
57
+ <td>HumanEval pass@1</td>
58
+ <td style="text-align: center">48.5</td>
59
+ <td style="text-align: center">49.1</td>
60
+ </tr>
61
+ <tr>
62
+ <td>HumanEval+ pass@1</td>
63
+ <td style="text-align: center">44.2</td>
64
+ <td style="text-align: center">46.3</td>
65
+ </tr>
66
+ </table>