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Daniel Han 2024-02-14 23:32:51 +11:00
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@ -66,4 +66,38 @@ TrainingArguments(
eval_steps = 1,
)
```
This will cause no OOMs and make it somewhat faster with no upcasting to float32.
This will cause no OOMs and make it somewhat faster with no upcasting to float32.
### GGUF quantization options
```python
# 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",
}
```