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Running
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Running
on
Zero
Update utils/models.py
Browse files- utils/models.py +44 -25
utils/models.py
CHANGED
@@ -1,7 +1,4 @@
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import os
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# Keep Dynamo error suppression
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import torch._dynamo
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torch._dynamo.config.suppress_errors = True
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os.environ["MKL_THREADING_LAYER"] = "GNU"
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import spaces
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@@ -17,8 +14,7 @@ from transformers import (
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BitNetForCausalLM
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)
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from .prompts import format_rag_prompt
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# from .shared import generation_interrupt
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models = {
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"Qwen2.5-1.5b-Instruct": "qwen/qwen2.5-1.5b-instruct",
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@@ -48,13 +44,13 @@ tokenizer_cache = {}
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model_names = list(models.keys())
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#
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@spaces.GPU
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@@ -62,7 +58,9 @@ def generate_summaries(example, model_a_name, model_b_name):
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"""
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Generates summaries for the given example using the assigned models sequentially.
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"""
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context_text = ""
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context_parts = []
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@@ -90,15 +88,18 @@ def generate_summaries(example, model_a_name, model_b_name):
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question = example.get("question", "")
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# Run model A
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summary_a = run_inference(models[model_a_name], context_text, question)
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# Run model B
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summary_b = run_inference(models[model_b_name], context_text, question)
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print("Both models completed successfully")
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return summary_a, summary_b
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@@ -106,8 +107,12 @@ def generate_summaries(example, model_a_name, model_b_name):
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def run_inference(model_name, context, question):
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"""
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Run inference using the specified model.
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Returns the generated text.
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"""
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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result = ""
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tokenizer_kwargs = {
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@@ -145,18 +150,25 @@ def run_inference(model_name, context, question):
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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print("REACHED HERE BEFORE pipe")
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print(f"Loading model {model_name}...")
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if "bitnet" in model_name.lower():
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bitnet_model = BitNetForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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)
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pipe = pipeline(
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"text-generation",
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model=bitnet_model,
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tokenizer=tokenizer,
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torch_dtype=torch.bfloat16,
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model_kwargs={
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"attn_implementation": "eager",
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@@ -189,14 +201,12 @@ def run_inference(model_name, context, question):
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)
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text_input = format_rag_prompt(question, context, accepts_sys)
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print(f"Starting generation for {model_name}")
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if "Gemma-3".lower() in model_name.lower():
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print("REACHED HERE BEFORE GEN")
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result = pipe(
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text_input,
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max_new_tokens=512,
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generation_kwargs={"skip_special_tokens": True}
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)[0]["generated_text"]
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result = result[-1]["content"]
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@@ -211,6 +221,7 @@ def run_inference(model_name, context, question):
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**tokenizer_kwargs,
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)
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model_inputs = model_inputs.to(model.device)
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input_ids = model_inputs.input_ids
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@@ -219,12 +230,16 @@ def run_inference(model_name, context, question):
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prompt_tokens_length = input_ids.shape[1]
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with torch.inference_mode():
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output_sequences = model.generate(
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input_ids=input_ids,
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attention_mask=attention_mask,
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max_new_tokens=512,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id
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)
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generated_token_ids = output_sequences[0][prompt_tokens_length:]
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@@ -238,10 +253,14 @@ def run_inference(model_name, context, question):
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# **tokenizer_kwargs,
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# ).to(bitnet_model.device)
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# with torch.inference_mode():
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# output_sequences = bitnet_model.generate(
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# **formatted,
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# max_new_tokens=512,
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# )
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# result = tokenizer.decode(output_sequences[0][formatted['input_ids'].shape[-1]:], skip_special_tokens=True)
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else: # For other models
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formatted = pipe.tokenizer.apply_chat_template(
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@@ -251,16 +270,16 @@ def run_inference(model_name, context, question):
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)
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input_length = len(formatted)
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outputs = pipe(
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formatted,
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max_new_tokens=512,
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generation_kwargs={"skip_special_tokens": True}
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)
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result = outputs[0]["generated_text"][input_length:]
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print(f"Generation completed for {model_name}")
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except Exception as e:
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print(f"Error in inference for {model_name}: {e}")
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print(traceback.format_exc())
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import os
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os.environ["MKL_THREADING_LAYER"] = "GNU"
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import spaces
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BitNetForCausalLM
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)
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from .prompts import format_rag_prompt
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from .shared import generation_interrupt
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models = {
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"Qwen2.5-1.5b-Instruct": "qwen/qwen2.5-1.5b-instruct",
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model_names = list(models.keys())
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# Custom stopping criteria that checks the interrupt flag
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class InterruptCriteria(StoppingCriteria):
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def __init__(self, interrupt_event):
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self.interrupt_event = interrupt_event
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def __call__(self, input_ids, scores, **kwargs):
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return self.interrupt_event.is_set()
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@spaces.GPU
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"""
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Generates summaries for the given example using the assigned models sequentially.
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"""
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if generation_interrupt.is_set():
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return "", ""
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context_text = ""
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context_parts = []
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question = example.get("question", "")
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if generation_interrupt.is_set():
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return "", ""
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# Run model A
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summary_a = run_inference(models[model_a_name], context_text, question)
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if generation_interrupt.is_set():
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return summary_a, ""
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# Run model B
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summary_b = run_inference(models[model_b_name], context_text, question)
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return summary_a, summary_b
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def run_inference(model_name, context, question):
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"""
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Run inference using the specified model.
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Returns the generated text or empty string if interrupted.
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"""
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# Check interrupt at the beginning
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if generation_interrupt.is_set():
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return ""
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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result = ""
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tokenizer_kwargs = {
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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# Check interrupt before loading the model
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if generation_interrupt.is_set():
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return ""
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print("REACHED HERE BEFORE pipe")
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print(f"Loading model {model_name}...")
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if "bitnet" in model_name.lower():
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bitnet_model = BitNetForCausalLM.from_pretrained(
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model_name,
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#device_map="auto",
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torch_dtype=torch.bfloat16,
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#trust_remote_code=True,
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)
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pipe = pipeline(
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"text-generation",
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model=bitnet_model,
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tokenizer=tokenizer,
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#device_map="auto",
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#trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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model_kwargs={
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"attn_implementation": "eager",
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)
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text_input = format_rag_prompt(question, context, accepts_sys)
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if "Gemma-3".lower() in model_name.lower():
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print("REACHED HERE BEFORE GEN")
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result = pipe(
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text_input,
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max_new_tokens=512,
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generation_kwargs={"skip_special_tokens": True},
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)[0]["generated_text"]
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result = result[-1]["content"]
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**tokenizer_kwargs,
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)
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model_inputs = model_inputs.to(model.device)
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input_ids = model_inputs.input_ids
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prompt_tokens_length = input_ids.shape[1]
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with torch.inference_mode():
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# Check interrupt before generation
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if generation_interrupt.is_set():
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return ""
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output_sequences = model.generate(
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input_ids=input_ids,
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attention_mask=attention_mask,
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max_new_tokens=512,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id # Addresses the warning
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)
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generated_token_ids = output_sequences[0][prompt_tokens_length:]
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# **tokenizer_kwargs,
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# ).to(bitnet_model.device)
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# with torch.inference_mode():
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# # Check interrupt before generation
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# if generation_interrupt.is_set():
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# return ""
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# output_sequences = bitnet_model.generate(
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# **formatted,
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# max_new_tokens=512,
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# )
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# result = tokenizer.decode(output_sequences[0][formatted['input_ids'].shape[-1]:], skip_special_tokens=True)
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else: # For other models
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formatted = pipe.tokenizer.apply_chat_template(
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)
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input_length = len(formatted)
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# Check interrupt before generation
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outputs = pipe(
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formatted,
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max_new_tokens=512,
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generation_kwargs={"skip_special_tokens": True},
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)
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# print(outputs[0]['generated_text'])
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result = outputs[0]["generated_text"][input_length:]
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except Exception as e:
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print(f"Error in inference for {model_name}: {e}")
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print(traceback.format_exc())
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