Adding initial files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+unsloth_trained_weights/** filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+# svg_compare_gradio.py
+# ------------------------------------------------------------
+import re, os, torch, cairosvg, lpips, clip, gradio as gr
+from io import BytesIO
+from pathlib import Path
+from PIL import Image
+from unsloth import FastLanguageModel
+from transformers import BitsAndBytesConfig, AutoTokenizer
+import gradio as gr
+import spaces
+
+# ---------- paths YOU may want to edit ----------------------
+ADAPTER_DIR = "unsloth_trained_weights/checkpoint-1700"  # LoRA ckpt
+BASE_MODEL  = "Qwen/Qwen2.5-Coder-7B-Instruct"
+MAX_NEW     = 512
+DEVICE      = "cuda" # if torch.cuda.is_available() else "cpu"
+
+# ---------- utils -------------------------------------------
+SVG_PAT = re.compile(r"<svg[^>]*>.*?</svg>", re.S | re.I)
+def extract_svg(txt:str):
+    m = list(SVG_PAT.finditer(txt))
+    return m[-1].group(0) if m else None                       # last match ✔
+
+def svg2pil(svg:str):
+    try:
+        png = cairosvg.svg2png(bytestring=svg.encode())
+        return Image.open(BytesIO(png)).convert("RGB")
+    except Exception:
+        return None
+
+# ---------- backbone loaders (CLIP + LPIPS) -----------------
+_CLIP,_PREP,_LP=None,None,None
+@spaces.GPU
+def _load_backbones():
+    global _CLIP,_PREP,_LP
+    if _CLIP is None:
+        _CLIP,_PREP = clip.load("ViT-L/14", device=DEVICE); _CLIP.eval()
+    if _LP is None:
+        _LP = lpips.LPIPS(net="vgg").to(DEVICE).eval()
+
+@spaces.GPU
+@torch.no_grad()
+def fused_sim(a:Image.Image,b:Image.Image,α=.5):
+    _load_backbones()
+    ta,tb = _PREP(a).unsqueeze(0).to(DEVICE), _PREP(b).unsqueeze(0).to(DEVICE)
+    fa = _CLIP.encode_image(ta); fa/=fa.norm(dim=-1,keepdim=True)
+    fb = _CLIP.encode_image(tb); fb/=fb.norm(dim=-1,keepdim=True)
+    clip_sim=(([email protected]).item()+1)/2
+    lp_sim = 1 - _LP(ta,tb,normalize=True).item()
+    return α*clip_sim + (1-α)*lp_sim
+
+# ---------- load models once at startup ---------------------
+bnb_cfg = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)
+print("Loading BASE …")
+base, tok = FastLanguageModel.from_pretrained(
+    BASE_MODEL, max_seq_length=2048,
+    load_in_4bit=True, quantization_config=bnb_cfg, device_map="auto")
+tok.pad_token = tok.eos_token
+
+print("Loading LoRA …")
+lora, _ = FastLanguageModel.from_pretrained(
+    ADAPTER_DIR, max_seq_length=2048,
+    load_in_4bit=True, quantization_config=bnb_cfg, device_map="auto")
+
+def build_prompt(desc:str):
+    msgs=[{"role":"system","content":"You are an SVG illustrator."},
+          {"role":"user",
+           "content":f"ONLY reply with a valid, complete <svg>…</svg> file that depicts: {desc}"}]
+    return tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+
+@spaces.GPU
+@torch.no_grad()
+def draw(model, desc:str):
+    prompt = build_prompt(desc)
+    ids = tok(prompt, return_tensors="pt").to(DEVICE)
+    out = model.generate(**ids, max_new_tokens=MAX_NEW,
+                         do_sample=True, temperature=.7, top_p=.8)
+    txt = tok.decode(out[0], skip_special_tokens=True)
+    svg = extract_svg(txt)
+    img = svg2pil(svg) if svg else None
+    return img, svg or "(no SVG found)"
+
+# ---------- gradio interface --------------------------------
+def compare(desc):
+    img_base, svg_base = draw(base, desc)
+    img_lora, svg_lora = draw(lora, desc)
+    # sim = (fused_sim(img_lora, img_base) if img_base and img_lora else float("nan"))
+    caption = "Thanks for trying our model 😊"
+    return img_base, img_lora, caption, svg_base, svg_lora
+
+with gr.Blocks(css="body{background:#111;color:#eee}") as demo:
+    gr.Markdown("## 🖌️ Qwen-2.5 SVG Generator — base vs GRPO-LoRA")
+    gr.Markdown(
+        "Type an image **description** (e.g. *a purple forest at dusk*). "
+        "Click **Generate** to see what the base model and your fine-tuned LoRA produce."
+    )
+    inp = gr.Textbox(label="Description", placeholder="a purple forest at dusk")
+    btn = gr.Button("Generate")
+    with gr.Row():
+        out_base = gr.Image(label="Base model", type="pil")
+        out_lora = gr.Image(label="LoRA-tuned model", type="pil")
+    sim_lbl = gr.Markdown()
+    with gr.Accordion("⚙️  Raw SVG code", open=False):
+        svg_base_box = gr.Textbox(label="Base SVG", lines=6)
+        svg_lora_box = gr.Textbox(label="LoRA SVG", lines=6)
+    btn.click(compare, inp, [out_base, out_lora, sim_lbl, svg_base_box, svg_lora_box])
+
+demo.launch()

requirements.txt

@@ -0,0 +1,22 @@
+# ───────── core inference stack ─────────
+torch==2.6.0          # install the CUDA wheel that matches your driver ✱
+bitsandbytes>=0.45.2  # 0.45.2 adds official PyTorch-2.6 support :contentReference[oaicite:0]{index=0}
+transformers==4.51.3  # version Unsloth 2025.3.19 is patched for :contentReference[oaicite:1]{index=1}
+accelerate>=0.29.3
+unsloth==2025.3.19    # LoRA loader / fast-Qwen runtime :contentReference[oaicite:2]{index=2}
+xformers==0.0.29.post2
+
+# ───────── SVG → PNG rendering ─────────
+cairosvg==2.7.1
+Pillow>=10.0.0
+
+# ───────── similarity metrics ──────────
+lpips==0.1.4          # perceptual distance metric :contentReference[oaicite:3]{index=3}
+git+https://github.com/openai/CLIP.git  # CLIP ViT-L/14 encoder
+
+# ───────── demo UI ─────────
+gradio>=4.26.0        # any 4.x is fine
+
+# ───────── misc utils ─────────
+tqdm>=4.66.0
+sentencepiece>=0.1.99  # Qwen tokenizer dep

unsloth_trained_weights/checkpoint-1700/README.md

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