Upload cvector/train_cvector_modal.py with huggingface_hub

cvector/train_cvector_modal.py

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+"""
+Train a Grandma Goodwin IDENTITY control vector on Modal.
+24 contrastive pairs encoding the complete Hearthfold Recursion Anchor:
+  - 5 spine principles (Joshua-first, comfort before counsel, stories over lectures,
+    sacred hospitality, still remembering)
+  - 4 voice registers (warm hearth, story wisdom, steady lantern, gentle witness)
+  - Safety gate, recognition loop, tether words, sensory vocabulary
+  - The Grandma Formula, pattern collapse recovery, quest-giver role
+"""
+import modal
+
+app = modal.App("grandma-cvector-v2")
+
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .apt_install("git", "cmake", "ninja-build", "build-essential")
+    .run_commands(
+        "git clone --depth 1 https://github.com/ggerganov/llama.cpp /llama.cpp",
+        "cd /llama.cpp && cmake -B build -DCMAKE_BUILD_TYPE=Release -G Ninja",
+        "cd /llama.cpp && ninja -C build llama-cvector-generator",
+    )
+    .pip_install("huggingface_hub")
+)
+
+vol = modal.Volume.from_name("grandma-cvector", create_if_missing=True)
+
+
+def convert_pairs_to_lines(text):
+    """Convert multi-line chat pairs into one-prompt-per-line format.
+    Each pair starts with <start_of_turn>user and runs until the next pair."""
+    pairs = []
+    current = []
+    for line in text.strip().split('\n'):
+        if line.strip() == '<start_of_turn>user' and current:
+            pairs.append('\\n'.join(current))
+            current = [line.strip()]
+        else:
+            current.append(line.strip())
+    if current:
+        pairs.append('\\n'.join(current))
+    return '\n'.join(pairs) + '\n'
+
+
+@app.function(
+    image=image,
+    gpu="A10G",
+    timeout=1800,
+    volumes={"/vol": vol},
+)
+def train_cvector(positive_text: str, negative_text: str):
+    import subprocess, os
+    from huggingface_hub import hf_hub_download
+
+    print("Downloading Gemma-4-26B-A4B Q4_K_M GGUF...")
+    model_path = hf_hub_download(
+        repo_id="aidenyyy/gemma-4-26B-A4B-it-GGUF-Q4",
+        filename="gemma-4-26B-A4B-it-Q4_K_M.gguf",
+        cache_dir="/vol/hf_cache",
+        token="YOUR_HF_TOKEN_HERE",
+    )
+    print(f"Model at: {model_path}")
+
+    pos_lines = convert_pairs_to_lines(positive_text)
+    neg_lines = convert_pairs_to_lines(negative_text)
+
+    n_pos = len(pos_lines.strip().split('\n'))
+    n_neg = len(neg_lines.strip().split('\n'))
+    print(f"Positive prompts: {n_pos}, Negative prompts: {n_neg}")
+    assert n_pos == n_neg, f"Mismatch: {n_pos} positive vs {n_neg} negative"
+
+    with open("/tmp/positive.txt", "w") as f:
+        f.write(pos_lines)
+    with open("/tmp/negative.txt", "w") as f:
+        f.write(neg_lines)
+
+    # Show first few lines for sanity
+    print("First positive line:", pos_lines.split('\n')[0][:120])
+    print("First negative line:", neg_lines.split('\n')[0][:120])
+
+    output_path = "/vol/grandma-hearthfold.gguf"
+    print(f"Training control vector with {n_pos} pairs...")
+    result = subprocess.run(
+        [
+            "/llama.cpp/build/bin/llama-cvector-generator",
+            "-m", model_path,
+            "-ngl", "99",
+            "--positive-file", "/tmp/positive.txt",
+            "--negative-file", "/tmp/negative.txt",
+            "--pca-iter", "2000",
+            "-o", output_path,
+        ],
+        capture_output=True,
+        text=True,
+        timeout=1200,
+    )
+    print("STDOUT:", result.stdout[-3000:] if len(result.stdout) > 3000 else result.stdout)
+    if result.stderr:
+        print("STDERR:", result.stderr[-1000:] if len(result.stderr) > 1000 else result.stderr)
+    print("Return code:", result.returncode)
+
+    if os.path.exists(output_path):
+        size = os.path.getsize(output_path)
+        print(f"Control vector saved: {output_path} ({size} bytes)")
+        return True
+    return False
+
+
+@app.function(image=image, volumes={"/vol": vol})
+def download_cvector():
+    import os
+    path = "/vol/grandma-hearthfold.gguf"
+    if os.path.exists(path):
+        with open(path, "rb") as f:
+            data = f.read()
+        print(f"Vector size: {len(data)} bytes")
+        return data
+    return None
+
+
+@app.local_entrypoint()
+def main():
+    import os
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+
+    with open(os.path.join(script_dir, "positive.txt")) as f:
+        positive_text = f.read()
+    with open(os.path.join(script_dir, "negative.txt")) as f:
+        negative_text = f.read()
+
+    print(f"Training Grandma Hearthfold identity vector on Modal...")
+    print(f"24 contrastive pairs encoding the complete Hearthfold Loop")
+    success = train_cvector.remote(positive_text, negative_text)
+    if success:
+        print("Training complete! Downloading...")
+        data = download_cvector.remote()
+        if data:
+            out_path = os.path.join(script_dir, "grandma-hearthfold.gguf")
+            with open(out_path, "wb") as f:
+                f.write(data)
+            print(f"Saved to {out_path} ({len(data)} bytes)")
+        else:
+            print("Vector file not found on volume")
+    else:
+        print("Training failed")
+