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broadfield-dev/
gemma-3-270m-it-vismem-rag-parrot-3-vismem-rag-0315-0820

Name: gemma-3-270m-it-vismem-rag-parrot-3-vismem-rag-0315-0820
Availability: InStock

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VisMem RAG Fine-tuned: `gemma-3-270m-it-vismem-rag-parrot-3-vismem-rag-0315-0820`

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-it-vismem-rag-parrot-3-vismem-kb-0315-0820/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-it-vismem-rag-parrot-3-vismem-rag-0315-0820")
print(pipe([
    {"role":"system","content":system},
    {"role":"user","content":your_question}
], max_new_tokens=200))

Config

{   "dataset_name": "openai/gsm8k",   "rag_columns": [     "question",     "answer"   ],   "question_col": "question",   "answer_col": "answer",   "split": "train",   "data_config": "main",   "total_kb_docs": 7473,   "vismem_dim": 384,   "vismem_width": 4800,   "vismem_height": 4800,   "kb_repo": "broadfield-dev/gemma-3-270m-it-vismem-rag-parrot-3-vismem-kb-0315-0820" }

broadfield-dev/gemma-3-270m-it-vismem-rag-parrot-3-vismem-rag-0315-0820

VisMem RAG Fine-tuned: gemma-3-270m-it-vismem-rag-parrot-3-vismem-rag-0315-0820

Inference

Config

broadfield-dev/
gemma-3-270m-it-vismem-rag-parrot-3-vismem-rag-0315-0820

VisMem RAG Fine-tuned: `gemma-3-270m-it-vismem-rag-parrot-3-vismem-rag-0315-0820`