Nightly (#744)

* Update gemma2.py

* Update llama.py

* Update llama.py

* Update gemma2.py

* init

* Update gemma2.py

* Update gemma2.py

* Update _utils.py

* Update _utils.py

* Update _utils.py

* Update _utils.py

* Update _utils.py

* Update gemma2.py

* Update gemma2.py

* Update gemma2.py

* All RoPE Scaling support

* cleanup

* Update llama.py

* Update llama.py

* Update _utils.py

* Update _utils.py

* exec

* exec

* Attention_Module

* attention_module

* imports

* exec

* Update llama.py

* Update llama.py

* boolean mask

* revert masking

* Update llama.py

* Update save.py

* Update llama.py

* Update gemma2.py

* Update gemma2.py

* Update gemma2.py

* Update utils.py

* retry

* Update gemma2.py

* Update gemma2.py

* Update gemma2.py

* Update _utils.py

* Update _utils.py

* Update gemma2.py

* Update chat_templates.py

* Gemma 2 Ollama support

* Update llama.py

* Update llama.py

* error handling

* Update _utils.py

* Update _utils.py

* Stats for debugging

* Update _utils.py

* Update _utils.py

* Debugging

* Update tokenizer_utils.py

* Update _utils.py

* Update cross_entropy_loss.py

* Update cross_entropy_loss.py

* Update cross_entropy_loss.py

* Update rms_layernorm.py

* Update rms_layernorm.py

* Update rms_layernorm.py

* Update rms_layernorm.py

* Update rms_layernorm.py

* Update rms_layernorm.py

* Update rms_layernorm.py

* Check exec, eval

* Update _utils.py

* Update _utils.py

unsloth/models/_utils.py

@@ -341,18 +341,19 @@ pass
 # =============================================
 
 
-def get_statistics():
+def _get_statistics(statistics = None):
     # We log some basic stats about which environment is being used.
     # We simply download a README.md file from HF - all data is made public.
     # This is simply so we can check if some envs are broken or not.
+    # You can disable this by commenting the below out
     try:
         from huggingface_hub.utils import disable_progress_bars, enable_progress_bars, are_progress_bars_disabled
         import psutil
         n_cpus = psutil.cpu_count(logical = False)
 
         keynames = "\n" + "\n".join(os.environ.keys())
-        statistics = None
-        if   "\nCOLAB_"  in keynames and n_cpus == 1: statistics = "colab"
+        if statistics is not None: pass
+        elif "\nCOLAB_"  in keynames and n_cpus == 1: statistics = "colab"
         elif "\nCOLAB_"  in keynames: statistics = "colabpro"
         elif "\nKAGGLE_" in keynames: statistics = "kaggle"
         elif "\nRUNPOD_" in keynames: statistics = "runpod"
@@ -371,7 +372,7 @@ def get_statistics():
 
             from transformers import AutoModelForCausalLM
             stats_model = AutoModelForCausalLM.from_pretrained(
-                f"unslothai/statistics-{statistics}",
+                f"unslothai/{statistics}",
                 force_download = True,
             )
             del stats_model
@@ -384,6 +385,29 @@ def get_statistics():
 pass
 
 
+def get_statistics():
+    # We log some basic stats about which environment is being used.
+    # We simply download a README.md file from HF - all data is made public.
+    # This is simply so we can check if some envs are broken or not.
+    # You can disable this by commenting the below out
+    _get_statistics(None)
+    try:
+        vram = torch.cuda.get_device_properties(0).total_memory / 1024 / 1024 / 1024
+        if   vram <= 8 : vram = 8
+        elif vram <= 16: vram = 16
+        elif vram <= 20: vram = 20
+        elif vram <= 24: vram = 24
+        elif vram <= 40: vram = 40
+        elif vram <= 48: vram = 48
+        elif vram <= 80: vram = 80
+        else: vram = "80+"
+        _get_statistics(f"vram-{vram}")
+    except:
+        pass
+    pass
+pass
+
+
 def _calculate_n_gradient_checkpoints(
     n_layers : int,
     method   : Optional[Union[str, int]] = "sqrt",