vbatts/llama.cpp
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add warning code when quantizing to Q4_0, Q4_1, Q5_0, or Q5_1

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chentyjpm 2024-08-15 14:20:07 +08:00
parent 57b79fda88
commit 7d261a9f96
1 changed files with 14 additions and 1 deletions
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15
convert_hf_to_gguf.py
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@ -322,6 +322,19 @@ class Model:
else: else:
raise ValueError(f"Unknown file type: {self.ftype.name}") raise ValueError(f"Unknown file type: {self.ftype.name}")
if data_qtype in [
gguf.GGMLQuantizationType.Q5_1, gguf.LlamaFileType.MOSTLY_Q5_0,
gguf.GGMLQuantizationType.Q4_1, gguf.LlamaFileType.MOSTLY_Q4_0,
]:
logger.warning("\n")
logger.warning("**************************************************************************************")
logger.warning("** WARNING: when quantizing to `Q4_0`, `Q4_1`, `Q5_0`, or `Q5_1`")
logger.warning("** is not equivalent to using `llama-quantize`")
logger.warning("** `llama-quantize` uses `Q4_K` and `Q6_K` for the token embeddings")
logger.warning("** but this code not")
logger.warning("**************************************************************************************")
logger.warning("\n")
try: try:
data = gguf.quants.quantize(data, data_qtype) data = gguf.quants.quantize(data, data_qtype)
except gguf.QuantError as e: except gguf.QuantError as e:
@ -3825,7 +3838,7 @@ def parse_args() -> argparse.Namespace:
parser.add_argument( parser.add_argument(
"--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "q4_0", "q4_1", "q5_0", "q5_1", "auto"], default="f16", "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "q4_0", "q4_1", "q5_0", "q5_1", "auto"], default="f16",
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, " help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, "
"q8_0 for Q8_0, q4_0 for Q4_0, q4_1 for Q4_1, q5_0 for Q5_0, q5_1 for Q5_1," "q8_0 for Q8_0, limited: q4_0 for Q4_0, q4_1 for Q4_1, q5_0 for Q5_0, q5_1 for Q5_1,"
" auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", " auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
) )
parser.add_argument( parser.add_argument(