Model: Successful load implementation

The goal of this series of commits is to have an implementation-agnostic
interface for models, thus being less reliant on HF Transformers for model
support. A model object will have a method for generation, a list of callbacks
to be run on every token generation, a list of samplers that will modify
probabilities, etc. Basically anything HF can do should be easily
implementable with the new interface :^)

Currently I've tested the loading of pre-downloaded models with
breakmodel between GPUs and that works, though essentially no testing
has been done in the larger scheme of things. Currently this is about
the only supported configuration, and generation isn't very functional.

utils.py

@@ -633,4 +633,20 @@ def get_missing_module_names(model: PreTrainedModel, names: List[str]) -> List[s
             else:
                 recurse(c[1], head=name + ".")
     recurse(model)
-    return missing_names
+    return missing_names
+
+class UIProgressBarFile(object):
+    """Write TQDM progress to the UI."""
+    def write(self, bar):
+        bar = bar.replace("\r", "").replace("\n", "").replace(chr(0), "")
+        if bar != "" and [ord(num) for num in bar] != [27, 91, 65]: #No idea why we're getting the 27, 1, 65 character set, just killing to so we can move on
+            #logger.info(bar)
+            print('\r' + bar, end='')
+            time.sleep(0.01)
+            try:
+                emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True, room="UI_1")
+            except:
+                pass
+        
+    def flush(self):
+        pass