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@@ -33,3 +33,18 @@ 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
+adapters/consciousness-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text
+adapters/davinci-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text
+adapters/empathy-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text
+adapters/hf_download/davinci/checkpoint-500/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+adapters/hf_download/davinci/checkpoint-939/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+adapters/hf_download/davinci/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+adapters/hf_download/newton/checkpoint-1000/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+adapters/hf_download/newton/checkpoint-1125/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+adapters/hf_download/newton/checkpoint-500/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+adapters/hf_download/newton/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+adapters/multi_perspective-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text
+adapters/newton-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text
+adapters/philosophy-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text
+adapters/quantum-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text
+adapters/systems_architecture-lora-f16.gguf filter=lfs diff=lfs merge=lfs -text

adapters/consciousness-lora-f16.gguf

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adapters/convert_peft_to_gguf.py

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+#!/usr/bin/env python3
+"""Convert PEFT LoRA safetensors to llama.cpp GGUF LoRA format.
+
+Lightweight converter — no torch/transformers dependency.
+Only needs: safetensors, gguf, numpy, struct.
+
+Matches the exact format produced by llama.cpp's convert_lora_to_gguf.py.
+"""
+
+import json
+import struct
+import sys
+from pathlib import Path
+import numpy as np
+
+# gguf uses its own writer
+from gguf import GGUFWriter, GGMLQuantizationType
+
+
+# PEFT tensor name -> GGUF tensor name mapping for LLama
+# PEFT:  base_model.model.model.layers.{i}.self_attn.{proj}.lora_{AB}.weight
+# GGUF:  blk.{i}.attn_{mapped_proj}.weight.lora_{ab}
+PROJ_MAP = {
+    "q_proj": "attn_q",
+    "k_proj": "attn_k",
+    "v_proj": "attn_v",
+    "o_proj": "attn_output",
+}
+
+
+def bf16_to_f16(data_bytes: bytes) -> np.ndarray:
+    """Convert bfloat16 raw bytes to float16 numpy array.
+
+    bf16: sign(1) + exp(8) + mantissa(7)
+    f16:  sign(1) + exp(5) + mantissa(10)
+
+    We go bf16 -> f32 -> f16 to avoid precision edge cases.
+    """
+    # Read as uint16 (same byte layout as bf16)
+    bf16 = np.frombuffer(data_bytes, dtype=np.uint16)
+    # Convert bf16 to f32: shift left 16 bits
+    f32_bytes = np.zeros(len(bf16), dtype=np.uint32)
+    f32_bytes[:] = bf16.astype(np.uint32) << 16
+    f32 = f32_bytes.view(np.float32)
+    # Convert f32 to f16
+    return f32.astype(np.float16)
+
+
+def read_safetensors(path: Path) -> dict:
+    """Read safetensors file, handling bf16 manually."""
+    with open(path, "rb") as f:
+        # Header: 8-byte little-endian uint64 = header size
+        header_size = struct.unpack("<Q", f.read(8))[0]
+        header_json = f.read(header_size)
+        header = json.loads(header_json)
+
+        data_start = 8 + header_size
+        tensors = {}
+
+        for name, info in header.items():
+            if name == "__metadata__":
+                continue
+            dtype = info["dtype"]
+            shape = info["shape"]
+            offsets = info["data_offsets"]
+            start, end = offsets
+
+            f.seek(data_start + start)
+            raw = f.read(end - start)
+
+            if dtype == "BF16":
+                arr = bf16_to_f16(raw).reshape(shape)
+            elif dtype == "F16":
+                arr = np.frombuffer(raw, dtype=np.float16).reshape(shape)
+            elif dtype == "F32":
+                arr = np.frombuffer(raw, dtype=np.float32).reshape(shape)
+                arr = arr.astype(np.float16)
+            else:
+                raise ValueError(f"Unsupported dtype: {dtype}")
+
+            tensors[name] = arr
+
+    return tensors
+
+
+def peft_name_to_gguf(peft_name: str) -> str | None:
+    """Map PEFT tensor name to GGUF tensor name.
+
+    Input:  base_model.model.model.layers.0.self_attn.q_proj.lora_A.weight
+    Output: blk.0.attn_q.weight.lora_a
+    """
+    parts = peft_name.split(".")
+    # Expected: base_model.model.model.layers.{i}.self_attn.{proj}.lora_{AB}.weight
+    try:
+        layer_idx = parts[4]  # layer number
+        proj = parts[6]       # q_proj, k_proj, etc.
+        lora_part = parts[7]  # lora_A or lora_B
+    except IndexError:
+        return None
+
+    gguf_proj = PROJ_MAP.get(proj)
+    if gguf_proj is None:
+        return None
+
+    ab = lora_part.lower()  # lora_a or lora_b
+    return f"blk.{layer_idx}.{gguf_proj}.weight.{ab}"
+
+
+def convert(adapter_dir: Path, output_path: Path, adapter_name: str):
+    """Convert a PEFT LoRA adapter to GGUF format."""
+    config_path = adapter_dir / "adapter_config.json"
+    safetensors_path = adapter_dir / "adapter_model.safetensors"
+
+    if not config_path.exists():
+        raise FileNotFoundError(f"No adapter_config.json in {adapter_dir}")
+    if not safetensors_path.exists():
+        raise FileNotFoundError(f"No adapter_model.safetensors in {adapter_dir}")
+
+    # Read config
+    with open(config_path) as f:
+        config = json.load(f)
+
+    lora_alpha = config.get("lora_alpha", 32)
+    lora_rank = config.get("r", 16)
+    print(f"  Config: rank={lora_rank}, alpha={lora_alpha}")
+
+    # Read tensors
+    print(f"  Reading safetensors...")
+    tensors = read_safetensors(safetensors_path)
+    print(f"  Loaded {len(tensors)} tensors")
+
+    # Create GGUF writer
+    writer = GGUFWriter(str(output_path), arch="llama")
+
+    # Write metadata (matching the newton GGUF format exactly)
+    writer.add_string("general.type", "adapter")
+    writer.add_string("adapter.type", "lora")
+    writer.add_string("general.name", adapter_name)
+    writer.add_uint32("general.base_model.count", 1)
+    writer.add_string("general.base_model.0.name", "Llama 3.1 8B Instruct")
+    writer.add_string("general.base_model.0.organization", "Meta Llama")
+    writer.add_string("general.base_model.0.repo_url",
+                       "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct")
+    writer.add_array("general.tags", [
+        "base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
+        "lora", "sft", "transformers", "trl", "text-generation",
+    ])
+    writer.add_float32("adapter.lora.alpha", float(lora_alpha))
+    writer.add_uint32("general.quantization_version", 2)
+
+    # Convert and add tensors
+    converted = 0
+    for peft_name, data in sorted(tensors.items()):
+        gguf_name = peft_name_to_gguf(peft_name)
+        if gguf_name is None:
+            print(f"  SKIP: {peft_name}")
+            continue
+
+        # GGUF LoRA expects F16 (type=1)
+        writer.add_tensor(gguf_name, data, raw_dtype=GGMLQuantizationType.F16)
+        converted += 1
+
+    print(f"  Converted {converted} tensors")
+
+    # Write file
+    writer.write_header_to_file()
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file()
+    writer.close()
+
+    size_mb = output_path.stat().st_size / 1024 / 1024
+    print(f"  Output: {output_path} ({size_mb:.1f} MB)")
+
+
+def main():
+    adapters_dir = Path("J:/codette-training-lab/adapters")
+    hf_dir = adapters_dir / "hf_download"
+
+    # Convert all adapters that have safetensors but no GGUF yet
+    to_convert = []
+    for name in ["empathy", "philosophy", "quantum",
+                  "consciousness", "multi_perspective", "systems_architecture"]:
+        src = hf_dir / name
+        dst = adapters_dir / f"{name}-lora-f16.gguf"
+        if src.exists() and (src / "adapter_model.safetensors").exists():
+            if dst.exists():
+                print(f"SKIP {name}: GGUF already exists")
+            else:
+                to_convert.append((name, src, dst))
+        else:
+            print(f"SKIP {name}: no safetensors found")
+
+    if not to_convert:
+        print("Nothing to convert!")
+        return
+
+    for name, src, dst in to_convert:
+        print(f"\nConverting {name}...")
+        try:
+            convert(src, dst, name)
+            print(f"OK: {name}")
+        except Exception as e:
+            print(f"FAIL: {name}: {e}")
+
+
+if __name__ == "__main__":
+    main()

adapters/davinci-lora-f16.gguf

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adapters/empathy-lora-f16.gguf

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