Create README.md

README.md

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+---
+license: apache-2.0
+base_model: amazingvince/Not-WizardLM-2-7B
+pipeline_tag: text-generation
+---
+
+# QuantFactory/Not-WizardLM-2-7B-GGUF
+This is quantized version of [amazingvince/Not-WizardLM-2-7B]() created using llama.cpp
+
+# Model Description
+
+<a href="https://colab.research.google.com/gist/pszemraj/d3d74ceab942722b49188606785e2bfd/not-wizardlm-2-7b-inference.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+Included is code ripped from fastchat with the expected chat templating.
+
+Also wiz.pdf is a pdf of the github blog showing the apache 2 release.
+Link to wayback machine included: https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/
+
+
+
+## example
+
+```python
+import dataclasses
+from enum import auto, Enum
+from typing import List, Tuple, Any
+
+
+class SeparatorStyle(Enum):
+    """Different separator style."""
+    SINGLE = auto()
+    TWO = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that keeps all conversation history."""
+    system: str
+    roles: List[str]
+    messages: List[List[str]]
+    offset: int
+    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
+    sep: str = "###"
+    sep2: str = None
+
+    # Used for gradio server
+    skip_next: bool = False
+    conv_id: Any = None
+
+    def get_prompt(self):
+        if self.sep_style == SeparatorStyle.SINGLE:
+            ret = self.system
+            for role, message in self.messages:
+                if message:
+                    ret += self.sep + " " + role + ": " + message
+                else:
+                    ret += self.sep + " " + role + ":"
+            return ret
+        elif self.sep_style == SeparatorStyle.TWO:
+            seps = [self.sep, self.sep2]
+            ret = self.system + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+            return ret
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+    def append_message(self, role, message):
+        self.messages.append([role, message])
+
+    def to_gradio_chatbot(self):
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset:]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def copy(self):
+        return Conversation(
+            system=self.system,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            conv_id=self.conv_id)
+
+    def dict(self):
+        return {
+            "system": self.system,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+            "sep": self.sep,
+            "sep2": self.sep2,
+            "conv_id": self.conv_id,
+        }
+
+
+
+conv = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the user's questions.",
+    roles=("USER", "ASSISTANT"),
+    messages=[],
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+conv.append_message(conv.roles[0], "Why would Microsoft take this down?")
+conv.append_message(conv.roles[1], None)
+prompt = conv.get_prompt()
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+result = model.generate(**inputs, max_new_tokens=1000)
+generated_ids = result[0]
+generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
+print(generated_text)
+
+```