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--- |
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license: other |
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license_name: other |
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license_link: LICENSE |
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--- |
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license for Llama 2 model checkpoints is Llama 2 Community license. \ |
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License for Lumina-T2I 5B checkpoints is Apache-2. |
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In this repo, you will find FP32 (original, un-changed), BF16 and FP16 PTH and FP32, BF16, FP16 safetensor files for Lumina T2I 5B text-to-image model. \ |
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BF16 pth file works fine, I plan to check the rest later. There could be some code missing in `safetensors` files due to it being removed during conversion, I don't know. If you try to run any of the files, let me know how they work. |
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You can also find un-gated files for Llama 2 7B 4-bit (bnb) and 16-bit. Both are simply copies of those files from unsloth repos. I have not run Lumina locally yet to confirm, but I believe both should work. |
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Script used for converting FP32 pth to FP16 pth |
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``` |
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import torch |
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# Load the FP32 model |
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fp32_model_path = "consolidated.00-of-01.pth" |
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fp32_model = torch.load(fp32_model_path, map_location='cpu') |
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# Convert the model to FP16 |
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fp16_model = {} |
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for key, value in fp32_model.items(): |
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if isinstance(value, torch.Tensor): |
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fp16_model[key] = value.half() |
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elif isinstance(value, dict): |
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fp16_model[key] = {k: v.half() if isinstance(v, torch.Tensor) else v for k, v in value.items()} |
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else: |
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fp16_model[key] = value |
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# Save the FP16 model |
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fp16_model_path = "consolidated.00-of-01_fp16.pth" |
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torch.save(fp16_model, fp16_model_path) |
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``` |
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Script used for converting FP32 pth to FP32, BF16, FP16 safetensors and BF16 pth |
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``` |
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import torch |
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from safetensors.torch import save_file, load_file |
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# Load the FP32 model |
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fp32_model_path = "consolidated.00-of-01.pth" |
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fp32_model = torch.load(fp32_model_path, map_location='cpu') |
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# Convert the model to BF16 |
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bf16_model = {} |
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for key, value in fp32_model.items(): |
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if isinstance(value, torch.Tensor): |
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bf16_model[key] = value.to(torch.bfloat16) |
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elif isinstance(value, dict): |
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bf16_model[key] = {k: v.to(torch.bfloat16) if isinstance(v, torch.Tensor) else v for k, v in value.items()} |
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else: |
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bf16_model[key] = value |
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# Convert the model to FP16 |
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fp16_model = {} |
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for key, value in fp32_model.items(): |
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if isinstance(value, torch.Tensor): |
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fp16_model[key] = value.half() |
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elif isinstance(value, dict): |
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fp16_model[key] = {k: v.half() if isinstance(v, torch.Tensor) else v for k, v in value.items()} |
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else: |
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fp16_model[key] = value |
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# Save the FP32 model in safetensors format |
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fp32_safetensors_path = "consolidated.00-of-01_fp32.safetensors" |
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save_file(fp32_model, fp32_safetensors_path) |
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# Save the BF16 model in safetensors format |
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bf16_safetensors_path = "consolidated.00-of-01_bf16.safetensors" |
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save_file(bf16_model, bf16_safetensors_path) |
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# Save the FP16 model in safetensors format |
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fp16_safetensors_path = "consolidated.00-of-01_fp16.safetensors" |
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save_file(fp16_model, fp16_safetensors_path) |
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# Save the BF16 model in .pth format |
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bf16_model_path = "consolidated.00-of-01_bf16.pth" |
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torch.save(bf16_model, bf16_model_path) |
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``` |
