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license: mit
tags: [rag, vismem, fine-tuning]
---
# VisMem RAG Fine-tuned: `gemma-3-270m-vismem-rag-QA-1-vismem-rag-0322-1623-vismem-rag-0323-1532`
## Inference
```python
import requests
from vismem_core import VisMem
from sentence_transformers import SentenceTransformer
from transformers import pipeline
# 1. Load VisMem
data = requests.get(
"https://huggingface.co/datasets/broadfield-dev/gemma-3-270m-vismem-rag-QA-1-vismem-rag-0322-1623-vismem-kb-0323-1532/resolve/main/vismem.png",
headers={"Authorization":"Bearer <HF_TOKEN>"}).content
mem = VisMem.from_png_bytes(data)
emb = SentenceTransformer('all-MiniLM-L6-v2')
# 2. RAG query
q_vec = emb.encode([your_question])[0]
results = mem.search(q_vec, k=3)
context = "\n---\n".join(results)
# 3. Prompt
system = (
"You are a helpful AI Assistant with visual memory.\n"
"### RAG MEMORY (Vector Database):\n"
"[Uploaded Doc]: None\n"
f"[Knowledge Base]: {context}\n"
"### EPISODIC MEMORY (Past Chat):\n"
"[History]: None"
)
pipe = pipeline("text-generation", model="broadfield-dev/gemma-3-270m-vismem-rag-QA-1-vismem-rag-0322-1623-vismem-rag-0323-1532")
print(pipe([
{"role":"system","content":system},
{"role":"user","content":your_question}
], max_new_tokens=200))
```
## Config
`{
"dataset_name": "AnonymousSub/MedQuAD_47441_Context_Question_Answer_Triples",
"rag_columns": [
"Contexts",
"Questions"
],
"question_col": "Questions",
"answer_col": "Answers",
"split": "train",
"data_config": null,
"total_kb_docs": 47441,
"vismem_dim": 384,
"vismem_width": 13056,
"vismem_height": 13056,
"kb_repo": "broadfield-dev/gemma-3-270m-vismem-rag-QA-1-vismem-rag-0322-1623-vismem-kb-0323-1532"
}`
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