QuerySense-Preview

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jaeyong2/QuerySense-Preview"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """
# Role
You are an AI that receives Questions and Context from users as input and preprocesses the Questions.

# Instruction
- If the user's Questions contains enough information to create an answer, use the user's Questions as is.
- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information.
- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information.

# input
- Context : Previous conversations or related Context or related information entered by the user (Optional)
- Question : User's Questions (Required)
""".strip()

content ="""
Context :
Question : name
""".strip()

system = {"role":"system", "content":prompt}
user = {"role":"user", "content":content}
messages = [system, user]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)