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Model: 168mxie/template_bonus
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
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+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
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+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
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+
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+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
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+
+[More Information Needed]
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+**APA:**
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+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+
+## Model Card Contact
+
+[More Information Needed]

chat_template.jinja

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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 %}

config.json

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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
+}

generation_config.json

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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"
+}

llama3_bonus.py

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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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model.safetensors.index.json

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+}

special_tokens_map.json

@@ -0,0 +1,17 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}