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Model: jadael/comma-v0.1-2t-GGUF
Source: Original Platform

convert_comma_to_gguf.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""
+Workaround script to convert Comma v0.1-2T to GGUF format.
+This uses transformers to load the model, then saves it in a format
+that llama.cpp can better handle.
+"""
+
+import sys
+import os
+from pathlib import Path
+
+try:
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    import torch
+except ImportError:
+    print("ERROR: Please install transformers: pip install transformers torch")
+    sys.exit(1)
+
+model_path = Path("comma-v0.1-2t")
+output_path = Path("comma-v0.1-2t-converted")
+
+print(f"Loading model from {model_path}...")
+print("This may take several minutes...")
+
+# Load model and tokenizer
+try:
+    model = AutoModelForCausalLM.from_pretrained(
+        str(model_path),
+        torch_dtype=torch.float16,
+        device_map="cpu",  # Keep on CPU for conversion
+        low_cpu_mem_usage=True
+    )
+    tokenizer = AutoTokenizer.from_pretrained(str(model_path))
+
+    print(f"Model loaded successfully!")
+    print(f"Vocabulary size: {len(tokenizer)}")
+    print(f"Model parameters: {sum(p.numel() for p in model.parameters()) / 1e9:.2f}B")
+
+    # Save in a clean format
+    output_path.mkdir(exist_ok=True)
+    print(f"\nSaving converted model to {output_path}...")
+
+    model.save_pretrained(str(output_path), safe_serialization=True)
+    tokenizer.save_pretrained(str(output_path))
+
+    print("\nConversion complete!")
+    print(f"Now try: python llama.cpp/convert_hf_to_gguf.py {output_path} --outfile comma-v0.1-2t.gguf --outtype q4_K_M")
+
+except Exception as e:
+    print(f"ERROR: {e}")
+    import traceback
+    traceback.print_exc()
+    sys.exit(1)