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Saving to GGUF

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

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

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

To push to Hugging Face hub:

model.push_to_hub_gguf("hf_username/directory", tokenizer, quantization_method = "q4_k_m")
model.push_to_hub_gguf("hf_username/directory", 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",
}

Running in Unsloth works well, but after exporting & running on other platforms, the results are poor

You might sometimes encounter an issue where your model runs and produces good results on Unsloth, but when you use it on another platform like Ollama or vLLM, the results are poor or you might get gibberish, endless/infinite generations or repeated outputs.

  • The most common cause of this error is using an incorrect chat template. It’s essential to use the SAME chat template that was used when training the model in Unsloth and later when you run it in another framework, such as llama.cpp or Ollama. When inferencing from a saved model, it's crucial to apply the correct template.

  • You must use the correct eos token. If not, you might get gibberish on longer generations.

  • It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses!

  • Use our conversational notebooks to force the chat template - this will fix most issues.

Saving to GGUF / vLLM 16bit crashes

You can try reducing the maximum GPU usage during saving by changing maximum_memory_usage.

The default is model.save_pretrained(..., maximum_memory_usage = 0.75). Reduce it to say 0.5 to use 50% of GPU peak memory or lower. This can reduce OOM crashes during saving.

How do I manually save to GGUF?

First save your model to 16bit via:

Compile llama.cpp from source like below:

Then, save the model to F16:

PreviousInference & DeploymentNextvLLM

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