Update README.md

README.md

@@ -4,7 +4,80 @@ license_name: other
 license_link: LICENSE
 ---
 license for Llama 2 model checkpoints is Llama 2 Community license. \
-License for Lumina-T2I 5B checkpoints is Apache-2. \
+License for Lumina-T2I 5B checkpoints is Apache-2.
 
 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. \
-None of the files were confirmed to work yet, I plan to check that later.
+None of the files were confirmed to work yet, I plan to check that 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.
+
+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.
+
+Script used for converting FP32 pth to FP16 pth
+
+```
+import torch
+
+# Load the FP32 model
+fp32_model_path = "consolidated.00-of-01.pth"
+fp32_model = torch.load(fp32_model_path, map_location='cpu')
+
+# Convert the model to FP16
+fp16_model = {}
+for key, value in fp32_model.items():
+    if isinstance(value, torch.Tensor):
+        fp16_model[key] = value.half()
+    elif isinstance(value, dict):
+        fp16_model[key] = {k: v.half() if isinstance(v, torch.Tensor) else v for k, v in value.items()}
+    else:
+        fp16_model[key] = value
+
+# Save the FP16 model
+fp16_model_path = "consolidated.00-of-01_fp16.pth"
+torch.save(fp16_model, fp16_model_path)
+```
+
+Script used for converting FP32 pth to FP32, BF16, FP16 safetensors and BF16 pth
+
+```
+import torch
+from safetensors.torch import save_file, load_file
+
+# Load the FP32 model
+fp32_model_path = "consolidated.00-of-01.pth"
+fp32_model = torch.load(fp32_model_path, map_location='cpu')
+
+# Convert the model to BF16
+bf16_model = {}
+for key, value in fp32_model.items():
+    if isinstance(value, torch.Tensor):
+        bf16_model[key] = value.to(torch.bfloat16)
+    elif isinstance(value, dict):
+        bf16_model[key] = {k: v.to(torch.bfloat16) if isinstance(v, torch.Tensor) else v for k, v in value.items()}
+    else:
+        bf16_model[key] = value
+
+# Convert the model to FP16
+fp16_model = {}
+for key, value in fp32_model.items():
+    if isinstance(value, torch.Tensor):
+        fp16_model[key] = value.half()
+    elif isinstance(value, dict):
+        fp16_model[key] = {k: v.half() if isinstance(v, torch.Tensor) else v for k, v in value.items()}
+    else:
+        fp16_model[key] = value
+
+# Save the FP32 model in safetensors format
+fp32_safetensors_path = "consolidated.00-of-01_fp32.safetensors"
+save_file(fp32_model, fp32_safetensors_path)
+
+# Save the BF16 model in safetensors format
+bf16_safetensors_path = "consolidated.00-of-01_bf16.safetensors"
+save_file(bf16_model, bf16_safetensors_path)
+
+# Save the FP16 model in safetensors format
+fp16_safetensors_path = "consolidated.00-of-01_fp16.safetensors"
+save_file(fp16_model, fp16_safetensors_path)
+
+# Save the BF16 model in .pth format
+bf16_model_path = "consolidated.00-of-01_bf16.pth"
+torch.save(bf16_model, bf16_model_path)
+```