a

app.py

@@ -4,71 +4,34 @@ from huggingface_hub import InferenceClient
 """
 Copied from inference in colab notebook
 """
-import torch
+# import torch
 
-# Monkey-patch to avoid CUDA initialization issues
-torch.cuda.get_device_capability = lambda *args, **kwargs: (0, 0)
+# # Monkey-patch to avoid CUDA initialization issues
+# torch.cuda.get_device_capability = lambda *args, **kwargs: (0, 0)
 
-from unsloth.chat_templates import get_chat_template
-from unsloth import FastLanguageModel
+# from unsloth.chat_templates import get_chat_template
+# from unsloth import FastLanguageModel
 
-# IMPORTING MODEL AND TOKENIZER ————————
+# # IMPORTING MODEL AND TOKENIZER ————————
 
-max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
-dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
-load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+# max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+# dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+# load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
 
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "llama_lora_model_1",
-    max_seq_length = max_seq_length,
-    dtype = dtype,
-    load_in_4bit = load_in_4bit,
-)
-
-tokenizer = get_chat_template(
-    tokenizer,
-    chat_template = "llama-3.1",
-)
-FastLanguageModel.for_inference(model) # Enable native 2x faster inference
-
-# RUNNING INFERENCE ————————————————————————
+# model, tokenizer = FastLanguageModel.from_pretrained(
+#     model_name = "llama_lora_model_1",
+#     max_seq_length = max_seq_length,
+#     dtype = dtype,
+#     load_in_4bit = load_in_4bit,
+# )
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    inputs = tokenizer.apply_chat_template(
-        messages,
-        tokenize = True,
-        add_generation_prompt = True, # Must add for generation
-        return_tensors = "pt",
-    )
-
-    outputs = model.generate(input_ids = inputs, max_new_tokens = max_tokens, use_cache = True,
-                            temperature = 1.5, min_p = 0.1)
-    response = tokenizer.batch_decode(outputs)
-
-    yield response
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-# client = InferenceClient("llama_lora_model_1")
+# tokenizer = get_chat_template(
+#     tokenizer,
+#     chat_template = "llama-3.1",
+# )
+# FastLanguageModel.for_inference(model) # Enable native 2x faster inference
 
+# # RUNNING INFERENCE ————————————————————————
 
 # def respond(
 #     message,
@@ -88,19 +51,56 @@ For more information on `huggingface_hub` Inference API support, please check th
 
 #     messages.append({"role": "user", "content": message})
 
-#     response = ""
-
-#     for message in client.chat_completion(
+#     inputs = tokenizer.apply_chat_template(
 #         messages,
-#         max_tokens=max_tokens,
-#         stream=True,
-#         temperature=temperature,
-#         top_p=top_p,
-#     ):
-#         token = message.choices[0].delta.content
-
-#         response += token
-#         yield response
+#         tokenize = True,
+#         add_generation_prompt = True, # Must add for generation
+#         return_tensors = "pt",
+#     )
+
+#     outputs = model.generate(input_ids = inputs, max_new_tokens = max_tokens, use_cache = True,
+#                             temperature = 1.5, min_p = 0.1)
+#     response = tokenizer.batch_decode(outputs)
+
+#     yield response
+
+"""
+For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+"""
+client = InferenceClient("llama_lora_model_1")
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
 
 
 """