Image-Text-to-Text
PEFT
Safetensors
lora
muse-glimmer
json
structured-output
api
tool-use
conversational
Instructions to use yogeshjog/muse-glimmer-json-api with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yogeshjog/muse-glimmer-json-api with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-models/Muse-Glimmer-30B") model = PeftModel.from_pretrained(base_model, "yogeshjog/muse-glimmer-json-api") - Notebooks
- Google Colab
- Kaggle
File size: 725 Bytes
1c04470 | 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 | {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Muse JSON API Response",
"type": "object",
"additionalProperties": false,
"required": [
"status",
"type",
"data",
"message",
"error",
"meta"
],
"properties": {
"status": {
"type": "integer",
"minimum": 100,
"maximum": 599
},
"type": {
"enum": [
"code",
"error",
"media",
"multimodal",
"response",
"tool_call",
"vision"
]
},
"data": {},
"message": {
"type": "string"
},
"error": {
"type": [
"object",
"null"
]
},
"meta": {
"type": "object"
}
}
} |