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---
library_name: transformers
tags: []
---
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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- if strftime_now is defined %}
{%- set date_string = strftime_now("%d %b %Y") %}
{%- else %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{{- "<|eot_id|>" }}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"custom_pipelines": {
"quizbowl-bonus": {
"default": {
"model": {
"pt": [
"meta-llama/Llama-3.2-3B-Instruct",
"main"
]
}
},
"impl": "llama3_bonus.BonusPipeline",
"pt": [
"LlamaForCausalLM"
],
"tf": [],
"type": "text"
}
},
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 24,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 32.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": true,
"torch_dtype": "float32",
"transformers_version": "4.52.4",
"use_cache": true,
"vocab_size": 128256
}

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{
"bos_token_id": 128000,
"do_sample": true,
"eos_token_id": [
128001,
128008,
128009
],
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "4.52.4"
}

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# %%
# ----------------------------------------------------------
# Custom Hugging-Face pipeline for the “bonus” split that refers to the existing models
# Task id : quizbowl-bonus
# Expected input keys : leadin, part, previous_parts ('text' and 'guess')
# Must return : answer, confidence, explanation
# ----------------------------------------------------------
import json_repair
import torch
from datasets import Dataset
from loguru import logger
from torch.nn import functional as F
from tqdm.auto import tqdm
from transformers import Pipeline, pipeline
from transformers.models.llama.modeling_llama import LlamaForCausalLM
from transformers.pipelines import PIPELINE_REGISTRY
def format_part(number: int, text: str, guess: str) -> str:
return f"\t * Part {number}: {text}\n\t * Model Guess: {guess}"
system_prompt = """
You are a quizbowl player. Given the a leadin and your responses to the previous related parts, provide the answer, a brief (1-2 sentences) explanation to the provided question along with your confidence in the guess.
The answer should be a single word or short phrase, and the explanation should be concise and relevant to the question.
The answer should be formatted in the below JSON format:
{
"answer": str,
"explanation": str,
"confidence": float (0-1 in the steps of 0.01)
"justification": str (optional justification for the confidence score)
}
The confidence should be a float between 0 and 1, representing your confidence in the answer.
"""
user_prompt_template = """
"Leadin: {leadin}
Question: {part}"{image_note}
What is being asked in the question? Provide a concise answer, a brief explanation, and your confidence in the guess along with justification."""
def _bonus_image_note(leadin_images, part_images) -> str:
li = leadin_images or []
pi = part_images or []
if not li and not pi:
return ""
return (
f"\n\n[This bonus includes {len(li)} leadin image(s) and {len(pi)} part image(s); "
"this text-only pipeline does not see pixels—use a VLM pipeline with "
"`leadin_images` / `part_images`.]"
)
def prepare_conversation(leadin, part, image_note: str = ""):
messages = [
{
"role": "system",
"content": system_prompt,
},
{
"role": "user",
"content": user_prompt_template.format(
leadin=leadin, part=part, image_note=image_note
),
},
]
return messages
def parse_output_text(output_text: str):
try:
start_index = output_text.find("{")
if start_index == -1:
raise ValueError("No JSON object found in the output text.")
output_text = output_text[start_index:]
json_data = json_repair.loads(output_text)
if isinstance(json_data, list):
json_data = json_data[0]
answer = json_data.get("answer", "").strip()
explanation = json_data.get("explanation", "").strip()
confidence = json_data.get("confidence", 0.0)
except Exception as e:
logger.warning(
f"Error parsing JSON: {e.__class__.__name__} - {e}. Got:\n{output_text}"
)
answer, explanation, confidence = "", "", 0.0
try:
confidence = float(confidence)
confidence = max(0.0, min(1.0, confidence))
except ValueError:
logger.warning(f"Invalid confidence value: {confidence}. Defaulting to 0.0.")
confidence = 0.0
return {
"answer": answer,
"explanation": explanation,
"confidence": confidence,
}
def postprocess_response(output_text, scores=None):
model_response = parse_output_text(output_text)
# Compute a confidence score by averaging the max softmax probabilities over generated tokens.
if scores is not None and len(scores) > 0:
probs = [F.softmax(score, dim=-1).max().item() for score in scores]
logit_confidence = float(sum(probs) / len(probs)) if probs else 0.0
model_response["confidence"] = (
model_response["confidence"] + logit_confidence
) / 2
return model_response
class BonusPipeline(Pipeline):
def __init__(self, model, tokenizer, **kwargs):
super().__init__(
model=model,
tokenizer=tokenizer,
**kwargs,
)
self.tokenizer.padding_side = "left"
self.tokenizer.pad_token = self.tokenizer.eos_token
def _sanitize_parameters(self, **kwargs):
# No additional parameters needed
return {}, {}, {}
def preprocess(self, inputs):
batch_size = len(inputs["leadin"])
leadin_imgs = inputs.get("leadin_images") or [[] for _ in range(batch_size)]
part_imgs = inputs.get("part_images") or [[] for _ in range(batch_size)]
conversations = []
for i in range(batch_size):
note = _bonus_image_note(leadin_imgs[i], part_imgs[i])
conversations.append(
prepare_conversation(inputs["leadin"][i], inputs["part"][i], image_note=note)
)
model_inputs = self.tokenizer.apply_chat_template(
conversations,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
padding=True,
return_tensors="pt",
)
return model_inputs
def _forward(self, model_inputs):
# Do not use output_scores=True: it materializes full-vocab logits each step and
# routinely OOMs mid-size GPUs (e.g. T4). postprocess() only uses decoded text.
with torch.no_grad():
full = self.model.generate(
**model_inputs,
max_new_tokens=64,
)
input_length = model_inputs["input_ids"].shape[1]
class _GenOut:
__slots__ = ("sequences",)
def __init__(self, sequences):
self.sequences = sequences
return _GenOut(full[:, input_length:])
def postprocess(self, model_outputs):
output_texts = self.tokenizer.batch_decode(
model_outputs.sequences, skip_special_tokens=True
)
records = []
for output_text in output_texts:
record = postprocess_response(output_text)
records.append(record)
return records
PIPELINE_REGISTRY.register_pipeline(
"quizbowl-bonus",
pipeline_class=BonusPipeline,
pt_model=LlamaForCausalLM,
default={
"pt": ("meta-llama/Llama-3.2-3B-Instruct", "main"),
},
type="text",
)
# %%
if __name__ == "__main__":
import os
import torch
from transformers import BitsAndBytesConfig
# Full precision (default): ``device_map="auto"`` only.
# Tight GPU (e.g. HF Space T4 with an 8B checkpoint): ``LLAMA3_BONUS_4BIT=1 pip install bitsandbytes`` first.
model_kwargs: dict = {"device_map": "auto"}
if os.environ.get("LLAMA3_BONUS_4BIT", "").strip().lower() in ("1", "true", "yes", "on"):
model_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
pipe = pipeline("quizbowl-bonus", trust_remote_code=True, model_kwargs=model_kwargs)
examples = [
{
"leadin": "This is a leadin.",
"part": "What is the capital of France?",
},
{
"leadin": "This is another leadin.",
"part": "What is the largest planet in our solar system?",
"previous_parts": [
{"text": "What is the smallest planet?", "guess": "Mercury"},
{"text": "What is the second smallest planet?", "guess": "Mars"},
],
},
{
"leadin": "This is a leadin with no previous parts.",
"part": "What is the chemical symbol for water?",
"previous_parts": [],
},
] * 5
dataset = Dataset.from_list(examples)
print("Dataset size:", len(dataset))
outputs = []
batch_size = 5
for batch in tqdm(dataset.batch(batch_size), desc="Processing batches"):
output = pipe(batch, batch_size=batch_size)
outputs.extend(output)

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