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199
README.md
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199
README.md
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---
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library_name: transformers
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tags: []
|
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---
|
||||
|
||||
# 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.
|
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|
||||
- **Developed by:** [More Information Needed]
|
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- **Funded by [optional]:** [More Information Needed]
|
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- **Shared by [optional]:** [More Information Needed]
|
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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|
||||
### Model Sources [optional]
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|
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<!-- Provide the basic links for the model. -->
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|
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- **Repository:** [More Information Needed]
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||||
- **Paper [optional]:** [More Information Needed]
|
||||
- **Demo [optional]:** [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. -->
|
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|
||||
### Direct Use
|
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|
||||
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Downstream Use [optional]
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||||
|
||||
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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|
||||
[More Information Needed]
|
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|
||||
### Out-of-Scope Use
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||||
|
||||
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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|
||||
[More Information Needed]
|
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|
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## Bias, Risks, and Limitations
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|
||||
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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|
||||
[More Information Needed]
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|
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### Recommendations
|
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|
||||
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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|
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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|
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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|
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## Training Details
|
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|
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### Training Data
|
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|
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<!-- 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. -->
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|
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[More Information Needed]
|
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|
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### Training Procedure
|
||||
|
||||
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
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|
||||
#### 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
|
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|
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#### Testing Data
|
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|
||||
<!-- This should link to a Dataset Card if possible. -->
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|
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[More Information Needed]
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|
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#### Factors
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|
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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|
||||
[More Information Needed]
|
||||
|
||||
#### Metrics
|
||||
|
||||
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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|
||||
[More Information Needed]
|
||||
|
||||
### Results
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Summary
|
||||
|
||||
|
||||
|
||||
## Model Examination [optional]
|
||||
|
||||
<!-- Relevant interpretability work for the model goes here -->
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||||
|
||||
[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).
|
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|
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
|
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- **Carbon Emitted:** [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
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Citation [optional]
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
|
||||
**BibTeX:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
**APA:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Glossary [optional]
|
||||
|
||||
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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||||
|
||||
[More Information Needed]
|
||||
|
||||
## More Information [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Authors [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Contact
|
||||
|
||||
[More Information Needed]
|
||||
93
chat_template.jinja
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93
chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
|
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{%- if not tools_in_user_message is defined %}
|
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{%- set tools_in_user_message = true %}
|
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{%- endif %}
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{%- if not date_string is defined %}
|
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{%- if strftime_now is defined %}
|
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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||||
{%- endif %}
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||||
{{- "<|eot_id|>" }}
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||||
{%- endif %}
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||||
{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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57
config.json
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57
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"custom_pipelines": {
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"quizbowl-bonus": {
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"default": {
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"model": {
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"pt": [
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"meta-llama/Llama-3.2-3B-Instruct",
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"main"
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]
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}
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},
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"impl": "llama3_bonus.BonusPipeline",
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"pt": [
|
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"LlamaForCausalLM"
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],
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"tf": [],
|
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"type": "text"
|
||||
}
|
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},
|
||||
"eos_token_id": [
|
||||
128001,
|
||||
128008,
|
||||
128009
|
||||
],
|
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"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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12
generation_config.json
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12
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"
|
||||
}
|
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239
llama3_bonus.py
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239
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
|
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# Expected input keys : leadin, part, previous_parts ('text' and 'guess')
|
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# 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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||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user