Update README.md
Browse files
README.md
CHANGED
@@ -1,3 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
# LLMPromptKit: LLM Prompt Management System
|
2 |
|
3 |
LLMPromptKit is a comprehensive library for managing, versioning, testing, and evaluating prompts for Large Language Models (LLMs). It provides a structured framework to help data scientists and developers create, optimize, and maintain high-quality prompts.
|
@@ -10,6 +32,34 @@ LLMPromptKit is a comprehensive library for managing, versioning, testing, and e
|
|
10 |
- **Evaluation Framework**: Measure prompt quality with customizable metrics
|
11 |
- **Advanced Templating**: Create dynamic prompts with variables, conditionals, and loops
|
12 |
- **Command-line Interface**: Easily integrate into your workflow
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
13 |
|
14 |
## Documentation
|
15 |
|
@@ -20,6 +70,7 @@ For detailed documentation, see the [docs](./docs) directory:
|
|
20 |
- [CLI Usage](./docs/cli_usage.md)
|
21 |
- [Advanced Features](./docs/advanced_features.md)
|
22 |
- [Integration Examples](./docs/integration_examples.md)
|
|
|
23 |
|
24 |
## Installation
|
25 |
|
|
|
1 |
+
---
|
2 |
+
library_name: llmpromptkit
|
3 |
+
title: LLMPromptKit
|
4 |
+
emoji: 🚀
|
5 |
+
tags:
|
6 |
+
- prompt-engineering
|
7 |
+
- llm
|
8 |
+
- nlp
|
9 |
+
- prompt-management
|
10 |
+
- huggingface
|
11 |
+
- version-control
|
12 |
+
- ab-testing
|
13 |
+
- evaluation
|
14 |
+
languages:
|
15 |
+
- python
|
16 |
+
license: mit
|
17 |
+
pipeline_tag: text-generation
|
18 |
+
datasets:
|
19 |
+
- none
|
20 |
+
|
21 |
+
---
|
22 |
+
|
23 |
# LLMPromptKit: LLM Prompt Management System
|
24 |
|
25 |
LLMPromptKit is a comprehensive library for managing, versioning, testing, and evaluating prompts for Large Language Models (LLMs). It provides a structured framework to help data scientists and developers create, optimize, and maintain high-quality prompts.
|
|
|
32 |
- **Evaluation Framework**: Measure prompt quality with customizable metrics
|
33 |
- **Advanced Templating**: Create dynamic prompts with variables, conditionals, and loops
|
34 |
- **Command-line Interface**: Easily integrate into your workflow
|
35 |
+
- **Hugging Face Integration**: Seamlessly test prompts with thousands of open-source models
|
36 |
+
|
37 |
+
## Hugging Face Integration
|
38 |
+
|
39 |
+
LLMPromptKit includes a powerful integration with Hugging Face models, allowing you to:
|
40 |
+
|
41 |
+
- Test prompts with thousands of open-source models
|
42 |
+
- Run evaluations with models like FLAN-T5, GPT-2, and others
|
43 |
+
- Compare prompt performance across different model architectures
|
44 |
+
- Access specialized models for tasks like translation, summarization, and question answering
|
45 |
+
|
46 |
+
```python
|
47 |
+
from llmpromptkit import PromptManager, PromptTesting
|
48 |
+
from llmpromptkit.integrations.huggingface import get_huggingface_callback
|
49 |
+
|
50 |
+
# Initialize components
|
51 |
+
prompt_manager = PromptManager()
|
52 |
+
testing = PromptTesting(prompt_manager)
|
53 |
+
|
54 |
+
# Get a HuggingFace callback
|
55 |
+
hf_callback = get_huggingface_callback(
|
56 |
+
model_name="google/flan-t5-base",
|
57 |
+
task="text2text-generation"
|
58 |
+
)
|
59 |
+
|
60 |
+
# Run tests with the model
|
61 |
+
test_results = await testing.run_test_cases(prompt_id="your_prompt_id", llm_callback=hf_callback)
|
62 |
+
```
|
63 |
|
64 |
## Documentation
|
65 |
|
|
|
70 |
- [CLI Usage](./docs/cli_usage.md)
|
71 |
- [Advanced Features](./docs/advanced_features.md)
|
72 |
- [Integration Examples](./docs/integration_examples.md)
|
73 |
+
- [Integration Examples](./docs/integration_examples.md)
|
74 |
|
75 |
## Installation
|
76 |
|