Saving to GGUF

Saving models to 16bit for GGUF so you can use it for Ollama, Jan AI, Open WebUI and more!

Locally

To save to GGUF, use the below to save locally:

model.save_pretrained_gguf("dir", tokenizer, quantization_method = "q4_k_m")
model.save_pretrained_gguf("dir", tokenizer, quantization_method = "q8_0")
model.save_pretrained_gguf("dir", tokenizer, quantization_method = "f16")

For to push to hub:

model.push_to_hub_gguf("hf_username/dir", tokenizer, quantization_method = "q4_k_m")
model.push_to_hub_gguf("hf_username/dir", tokenizer, quantization_method = "q8_0")

All supported quantization options for quantization_method are listed below:

# https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/quantize.cpp#L19
# From https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html
ALLOWED_QUANTS = \
{
    "not_quantized"  : "Recommended. Fast conversion. Slow inference, big files.",
    "fast_quantized" : "Recommended. Fast conversion. OK inference, OK file size.",
    "quantized"      : "Recommended. Slow conversion. Fast inference, small files.",
    "f32"     : "Not recommended. Retains 100% accuracy, but super slow and memory hungry.",
    "f16"     : "Fastest conversion + retains 100% accuracy. Slow and memory hungry.",
    "q8_0"    : "Fast conversion. High resource use, but generally acceptable.",
    "q4_k_m"  : "Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K",
    "q5_k_m"  : "Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K",
    "q2_k"    : "Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.",
    "q3_k_l"  : "Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K",
    "q3_k_m"  : "Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K",
    "q3_k_s"  : "Uses Q3_K for all tensors",
    "q4_0"    : "Original quant method, 4-bit.",
    "q4_1"    : "Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.",
    "q4_k_s"  : "Uses Q4_K for all tensors",
    "q4_k"    : "alias for q4_k_m",
    "q5_k"    : "alias for q5_k_m",
    "q5_0"    : "Higher accuracy, higher resource usage and slower inference.",
    "q5_1"    : "Even higher accuracy, resource usage and slower inference.",
    "q5_k_s"  : "Uses Q5_K for all tensors",
    "q6_k"    : "Uses Q8_K for all tensors",
    "iq2_xxs" : "2.06 bpw quantization",
    "iq2_xs"  : "2.31 bpw quantization",
    "iq3_xxs" : "3.06 bpw quantization",
    "q3_k_xs" : "3-bit extra small quantization",
}

Manual Saving

First save your model to 16bit:

model.save_pretrained_merged("merged_model", tokenizer, save_method = "merged_16bit",)

Then use the terminal and do:

git clone --recursive https://github.com/ggerganov/llama.cpp
make clean -C llama.cpp
make all -j -C llama.cpp
pip install gguf protobuf

python llama.cpp/convert-hf-to-gguf.py FOLDER --outfile OUTPUT --outtype f16

Or follow the steps at https://rentry.org/llama-cpp-conversions#merging-loras-into-a-model using the model name "merged_model" to merge to GGUF.