Originally created by @robertvazan on GitHub (Dec 30, 2024).
Feature Request
Is your feature request related to a problem? Please describe.
Smaller models like qwen2.5:0.5b usually fail to produce tags with the current default template.
Describe the solution you'd like
The template below works better thanks to examples and more conventional formatting:
Generate 1-3 broad tags categorizing the main themes of the chat history, along with 1-3 more specific subtopic tags.
JSON format: { "tags": ["tag1", "tag2", "tag3"] }
Examples:
- { "tags": ["Programming", "Java", "Unit tests"] }
- { "tags": ["Python", "Documentation", "Server"] }
- { "tags": ["Science", "Article summary", "Quantum computers"] }
Guidelines:
- Start with high-level domains (e.g. Science, Technology, Philosophy, Arts, Politics, Business, Health, Sports, Entertainment, Education)
- Consider including relevant subfields/subdomains if they are strongly represented throughout the conversation
- If content is too short (less than 3 messages) or too diverse, use only ["General"]
- Use the chat's primary language; default to English if multilingual
- Prioritize accuracy over specificity
RESPOND ONLY WITH THE JSON CODE.
<chat_history>
{{MESSAGES:END:6}}
</chat_history>
I actually have other customizations in the template (notably {{prompt:middletruncate:1000}} instead of {{MESSAGES:END:6}}), but examples and the ALL CAPS at the end are probably the key changes. Examples listed in this template are rather random and they could use broadening into non-technical areas, but the point is to focus on examples that small models understand instead of lengthy instructions that tend to be challenging even for frontier models.
Originally created by @robertvazan on GitHub (Dec 30, 2024).
# Feature Request
**Is your feature request related to a problem? Please describe.**
Smaller models like `qwen2.5:0.5b` usually fail to produce tags with the current default template.
**Describe the solution you'd like**
The template below works better thanks to examples and more conventional formatting:
```
Generate 1-3 broad tags categorizing the main themes of the chat history, along with 1-3 more specific subtopic tags.
JSON format: { "tags": ["tag1", "tag2", "tag3"] }
Examples:
- { "tags": ["Programming", "Java", "Unit tests"] }
- { "tags": ["Python", "Documentation", "Server"] }
- { "tags": ["Science", "Article summary", "Quantum computers"] }
Guidelines:
- Start with high-level domains (e.g. Science, Technology, Philosophy, Arts, Politics, Business, Health, Sports, Entertainment, Education)
- Consider including relevant subfields/subdomains if they are strongly represented throughout the conversation
- If content is too short (less than 3 messages) or too diverse, use only ["General"]
- Use the chat's primary language; default to English if multilingual
- Prioritize accuracy over specificity
RESPOND ONLY WITH THE JSON CODE.
<chat_history>
{{MESSAGES:END:6}}
</chat_history>
```
I actually have other customizations in the template (notably `{{prompt:middletruncate:1000}}` instead of `{{MESSAGES:END:6}}`), but examples and the ALL CAPS at the end are probably the key changes. Examples listed in this template are rather random and they could use broadening into non-technical areas, but the point is to focus on examples that small models understand instead of lengthy instructions that tend to be challenging even for frontier models.
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Originally created by @robertvazan on GitHub (Dec 30, 2024).
Feature Request
Is your feature request related to a problem? Please describe.
Smaller models like
qwen2.5:0.5busually fail to produce tags with the current default template.Describe the solution you'd like
The template below works better thanks to examples and more conventional formatting:
I actually have other customizations in the template (notably
{{prompt:middletruncate:1000}}instead of{{MESSAGES:END:6}}), but examples and the ALL CAPS at the end are probably the key changes. Examples listed in this template are rather random and they could use broadening into non-technical areas, but the point is to focus on examples that small models understand instead of lengthy instructions that tend to be challenging even for frontier models.