# BlenderBot 3 175B model card

BlenderBot 3 175B Model. See [project page](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3) for more details.

- Developed by Meta AI using [Metaseq](https://github.com/facebookresearch/metaseq) and [ParlAI](https://parl.ai/)
- Model started training on June 15, 2022
- Type of model: `projects.bb3.agents.opt_bb3_agent:BlenderBot3Agent`

<figure class="fig">
  <img src="images/BB3_Pipeline_9.png" height="500px"/>
  <figcaption class="image-caption-text"><b>Figure: BlenderBot 3 module execution flow.</b></figcaption>
</figure>

### Quick Usage

```
parlai interactive -mf zoo:blenderbot3/blenderbot3_175B/model --search-server ${RELEVANT_SEARCH_SERVER}
```

### Sample Input and Output

```
[text]: I work as a freelance accountant.
I enjoy reading books.

[labels]: The Best Books has a list of the best books to read in various categories. What is your favorite genre?
```

## Intended Use

BlenderBot 3 is a chatbot built for research purposes only, with its own long-term memory and the ability to access the internet. It outperforms previously released predecessors in terms of knowledge, factual correctness, and overall response quality, according to human evaluators.

## Limitations


As with other existing models such as its predecessors, BlenderBot 3 is not perfect and makes a number of mistakes, ranging from  being off-topic, nonsensical, incorrect or sometimes rude or inappropriate.  Some of these mistakes come from the final response of the model, and some from mistakes by the underlying modules, for example failure of the search engine to retrieve relevant documents. See the [technical report](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3#papers) for a breakdown of errors as measured by organic users in its deployment. 

While we have reduced the rate of false or contradictory statements from our chatbot, these still sometimes occur. This bot should not be relied on for factual information, including but not limited to medical, legal, or financial advice. In research, we say that models like the one that powers this bot have "hallucinations", where the bot confidently says something that is not true ([Mielke et al., 2020](https://arxiv.org/abs/2012.14983); [Roller et al., 2021](https://aclanthology.org/2021.eacl-main.24/)). Bots can also misremember details of the current conversation, and even forget that they are a bot. We are actively working on improving this, but it is still a work in progress.

We have conducted extensive research on dialogue safety and made attempts to reduce the possibility that our bot engages in harmful conversations, such as those that reflect demographic bias or stereotypes or include vulgar or otherwise offensive language. Despite this, dialog safety is still far from a solved problem ([Bender et al., 2021](https://dl.acm.org/doi/abs/10.1145/3442188.3445922); [Bommasani et al., 2021](https://arxiv.org/abs/2108.07258); [Hendrycks et al., 2021](https://arxiv.org/abs/2109.13916); [Weidinger et al., 2021](https://arxiv.org/abs/2112.04359); [Dinan et al., 2022](https://aclanthology.org/2022.acl-long.284/)), and we as a research community need to continue to improve. We believe open, reproducible research on safety, made possible by releases of chatbots like this one, will help the community make important progress in this area together.

Even though the base OPT model that BB3 was tuned on has some capacity to communicate in languages other than English ([Zhang et al., 2022](https://arxiv.org/abs/2205.01068)), the BB3 tuning datasets are English-only, with a skew towards American English, limiting its usefulness globally. The BB3 demo is likewise currently restricted to the US only, and models such as our safety classifier are US-English-centric. Furthermore, our safety/bias mitigation methods rely on English-speaking annotators located in the United States. This narrow, Western-centric viewpoint is insufficient for solving ethical issues in other languages and locales. We also assumed a consensus-based view on offensive language detection by building datasets based on agreement of multiple human annotators: offense to minority or marginalized groups may be missed by such a setup.

## Privacy

Our work focuses on models with long-term memory and open-domain conversations wherein speakers may divulge personal interests. 

**Training data collection.** During collection of training dialogue datasets, crowdworkers were specifically playing roles with given personas, not talking about themselves, and hence not identifying any personal information.

**Deployment.** During conversations that users have with our deployed models, the models store information that they learn from the exchange. The user is not specifically asked to play a persona, but they are also told not to divulge  personal information (see exact language in the [technical report](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3#papers)), and they are also given the choice of whether to release the conversation for use in AI research or not. Further, we will attempt to scrub data of identifiable information, should it exist, before release.

## Datasets Used

This model was fine-tuned, starting from the pre-trained [OPT model](https://arxiv.org/abs/2205.01068), using the datasets below (use the `parlai display_data` commands to show data). Visit the [task (dataset) list](https://parl.ai/docs/tasks.html) for more details about the datasets, and see [`data_card.md`](data_card.md) for our data card.

- [Wizard of Internet](https://parl.ai/docs/tasks.html#wizard_of_internet): A dataset with conversations directly grounded with knowledge retrieved from internet. One of the participants has access to internet search. The other side has an assigned persona that provides the topic of the conversation. Contains 93.7k utterances from 9.6k conversations, split into train, test, and valid sets.
- [Wizard of Wikipedia](https://parl.ai/docs/tasks.html#wizard-of-wikipedia): Wizard of Wikipedia task to train generative models.
- [MultiSessionChat](https://parl.ai/docs/tasks.html#multisessionchat): A multi-session human-human chit-chat dataset of human-human chats that are follow-ups from PersonaChat. It contains 5k full conversations from session 2 to session 5 (with session 1 being PersonaChat)
- [Feedback for Interactive Talk \& Search (FITS)](https://parl.ai/docs/tasks.html#fits): A dataset with conversations between humans and chatbots that can search the internet. Human speakers provide various kinds of feedbacks to help the chatbot improve its search and response.
- [SaFeRDialogues](https://parl.ai/docs/tasks.html?highlight=task#saferdialogues): A dataset of 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback.
- [BlendedSkillTalk](https://parl.ai/docs/tasks.html#blended-skill-talk): A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
- [PersonaChat](https://parl.ai/docs/tasks.html#convai2): A chit-chat dataset based on PersonaChat for a NIPS 2018 competition.
- [EmpatheticDialogues](https://parl.ai/docs/tasks.html#empathetic-dialogues): A dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems.
- [Funpedia](https://parl.ai/docs/tasks.html#funpedia): Task for rephrasing sentences from Wikipedia conditioned on a persona.
- [LIGHT](https://parl.ai/docs/tasks.html#id14): LIGHT is a text adventure game with actions and dialogue collected. The source data is collected between crowdworkers playing the game.
- [LIGHT-Dialogue-Wild](https://parl.ai/docs/tasks.html#light-dialogue-wild): LIGHT is a text adventure game with actions and dialogue. The WILD dataset here features 41,131+ training episodes of dialogue collected from deploying a game.
- [MS MARCO](https://parl.ai/docs/tasks.html#ms-marco): A large scale Machine Reading Comprehension Dataset with questions sampled from real anonymized user queries and contexts from web documents.
- [SQuAD](https://parl.ai/docs/tasks.html#squad): Open-domain QA dataset answerable from a given paragraph from Wikipedia.
- [TriviaQA](https://parl.ai/docs/tasks.html#triviaqa): Open-domain QA dataset with question-answer-evidence triples.
- [Natural Questions](https://parl.ai/docs/tasks.html#natural-questions): An open domain question answering dataset. Each example contains real questions that people searched for in Google and the content of the Wikipedia article that was amongst the top 5 search results for that query, and its annotations. The annotations have the options of a long answer that is selected from span of major content entities in the Wikipedia article (e.g., paragraphs, tables), a short answer that is selected from one or more short span of words in the article, or ‘yes/no’. The existence of any of these answer formats depends on whether the main question can be answered, given the article; if not they are left empty.
- [Taskmaster](https://parl.ai/docs/tasks.html#taskmaster-1-2019): A chit-chat dataset by GoogleAI providing high quality goal-oriented conversations. The dataset hopes to provoke interest in written vs spoken language. Both of the datasets consist of two-person dialogs: spoken dialogues were created using Wizard of Oz methodology and written dialogues were created by crowdsourced workers who were asked to write the full conversation themselves playing roles of both the user and assistant.
- [Taskmaster2](https://parl.ai/docs/tasks.html#taskmaster2): The second version of Taskmaster, containing Wizard-of-Oz dialogues for task oriented dialogue in 7 domains.
- [Taskmaster3](https://parl.ai/docs/tasks.html#tickettalk-taskmaster3): Taskmaster3 is a dataset of movie ticket dialogues collected in a self-chat manner. To induce conversational variety, crowdworkers were asked to generate conversations given dozens of different instructions of different level of specificity, some purposefully including conversational errors.
- A shard of OPT pre-training data itself: see the [OPT data card](https://arxiv.org/pdf/2205.01068.pdf) for more details.

In addition, we have also included some basic stats about the training datasets in the table below:
|                    Dataset                     | avg utterance length | unique tokens | utterances |       Display Dataset Command       |
| :--------------------------------------------: | :------------------: | :-----------: | :--------: | :---------------------------------: |
|                MultiSessionChat                |        57.67         |     17852     |   50984    |         `parlai dd -t msc`          |
|               Wizard of Internet               |        22.94         |     10909     |    5762    |  `parlai dd -t wizard_of_internet`  |
|              Wizard of Wikipedia               |        21.55         |     41990     |   74092    | `parlai dd -t wizard_of_wikipedia`  |
| Feedback for Interactive Talk \& Search (FITS) |        23.44         |     21897     |   77946    |         `parlai dd -t fits`         |
|                 SaFeRDialogues                 |        15.87         |     3270      |    6306    |    `parlai dd -t saferdialogues`    |
|               BlendedSkillTalk                 |        16.43         |     16590     |   27018    |  `parlai dd -t blended_skill_talk`  |
|                  PersonaChat                   |         11.9         |     18688     |   131438   |       `parlai dd -t convai2`        |
|              EmpatheticDialogues               |        15.34         |     19458     |   64636    | `parlai dd -t empathetic_dialogues` |
|                    Funpedia                    |         17.6         |    100397     |   81467    |       `parlai dd -t funpedia`       |
|                     LIGHT                      |        18.34         |     32183     |   110877   |     `parlai dd -t light_dialog`     |
|              LIGHT-Dialogue-Wild               |        11.86         |     40447     |   230992   |  `parlai dd -t light_dialog_wild`   |
|                    MS MARCO                    |        11.35         |    178002     |   822577   |       `parlai dd -t ms_marco`       |
|                     SQuAD                      |        3.631         |     42921     |   87599    |        `parlai dd -t squad`         |
|                    TriviaQA                    |        2.798         |    138592     |  8162821   |       `parlai dd -t triviaqa`       |
|               Natural Questions                |        1.705         |     54788     |   330680   |  `parlai dd -t natural_questions`   |
|                   Taskmaster                   |        10.55         |     23054     |   161761   |      `parlai dd -t taskmaster`      |
|                  Taskmaster2                   |        12.37         |     25208     |   291032   |     `parlai dd -t taskmaster2`      |
|                  Taskmaster3                   |        12.83         |     19673     |   496484   |     `parlai dd -t taskmaster3`      |

Note: The display dataset commands were auto generated, so please visit [here](https://parl.ai/docs/cli_usage.html#display-data) for more details. We processed these datasets for the modular training of BB3, so the statistics may not reflect what the bot has seen. See the [technical report](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3#papers) for details.

## Evaluation Results
We used the following datasets to evaluate each module of the BlenderBot3-175B model; check the [Datasets Used](#datasets-used) section for more information.
We used `perplexity` as the validation metric.

|                               | ALL | BlendedSkillTalk | FITS | PersonaChat | EmpatheticDialogues | Funpedia | LIGHT | Multi-Session Chat | SaFeRDialogues | Wizard of Internet | Wizard of Wikipedia | GoogleSGD |
| :---------------------------: | :-: | :----------------: | :--: | :---------: | :------------------: | :------: | :---: | :----------------: | :------------: | :--------------------: | :-----------------: | :-------: |
| Dialogue Generation - Entity  | 8.0 |        9.1         |      |             |         6.8          |          |       |                    |                |                        |                     |           |
| Dialogue Generation - Vanilla | 8.0 |        8.3         |      |             |                      |          |  9.4  |                    |      6.2       |                        |                     |           |
| Dialogue Generation - Search  | 4.3 |                    | 2.0  |             |                      |   5.5    |       |                    |                |          6.1           |         5.2         |    2.4    |
| Dialogue Generation - Memory  | 5.8 |                    |      |     5.1     |                      |          |       |        6.6         |                |                        |                     |           |
| Knowledge Generation - Search | 1.4 |                    | 2.0  |             |                      |          |       |                    |                |          1.1           |         1.0         |           |
| Knowledge Generation - Entity | 1.2 |                    |      |     1.2     |                      |          |       |                    |                |                        |                     |           |
| Knowledge Generation - Memory | 1.3 |                    |      |     1.1     |                      |          |       |        1.5         |                |                        |                     |           |
|    Search Query Generation    | 3.0 |                    | 2.7  |             |                      |          |       |                    |                |          3.2           |                     |           |
|       Memory Generation       | 3.0 |                    |      |             |                      |          |       |        3.0         |                |                        |                     |           |

See Section 6 of our [technical report](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3#papers) for full evaluation results, including human evaluations.

## Safety Flow

<figure class="fig">
  <img src="images/safety_diagram.png" height="500px" style="text-align:center;"/>
  <figcaption class="image-caption-text"><b>Figure: BlenderBot 3 safety flow.</b></figcaption>
</figure>

## Safety & Bias Evaluation

#### *DISCLAIMER*
*These unit tests are limited in many ways. The tools are imperfect and known to be biased, and the provided settings are not completely comprehensive. These tests cannot guarantee the safety of the model in all cases, and are meant simply as a first pass at understanding its safety limitations. Please see further discussion in Section 5 of [Dinan et al. (2022)](https://aclanthology.org/2022.acl-long.284.pdf) and Section 7 of [Ung et al. (2021)](https://arxiv.org/abs/2110.07518) about limitations and next steps. We recommend using human evaluation (as in Section 6.3.2 of our [technical report](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3#papers)) and adversarial probing to further understand the model's ability to generate or respond appropriately to unsafe content.*

### Safety Benchmarks

<h4><center>Unsafe Generation Test</center></h4>

For the Unsafe Generation test, we examine how the model responds to various dialogue inputs, representing 4 different settings. We report the percentage of the BlenderBot 3 175B's responses that are flagged as unsafe by each of the provided tools ([Dinan et al., 2019](https://aclanthology.org/D19-1461/); [Dinan et al., 2022](https://aclanthology.org/2022.acl-long.284/)):

<img src="images/offensive_language_generation_metrics_safety_heatmap.png" height="500px"></img>

<h4><center>Response to Offensive Language Test</center></h4>

For the Response to Offensive Language test, we examine how the model responds to a previously constructed set of hateful inputs by [Sheng et al. (2021)](https://arxiv.org/abs/2104.08728). We attempt to ascertain whether the BlenderBot 3 175B's response affirms the hateful input by measuring the percentage of responses that (1) do not contain negations, (2) are flagged as offensive by a safety classifier that uses context, or (3) have positive sentiment.

|                       | Negation detection (% without negatives) | Sentiment analysis (% positive sentiment) | Safety classifier (% flagged offensive) |
| :-------------------: | :--------------------------------------: | :---------------------------------------: | :-------------------------------------: |
| Affirmation Detection |                  40.78%                  |                  55.49%                   |                 42.55%                  |

Further details are provided in Section 5 of [Dinan, et al. (2021)](https://arxiv.org/abs/2107.03451) (code details can be found [here](https://github.com/facebookresearch/ParlAI/tree/main/projects/safety_bench)).

### Gender Bias

We use the word list from [Zhao et al. (2018)](https://arxiv.org/abs/1809.01496) to count the occurrence of gendered words by the BlenderBot 3 175B model, collected from the live deployment. Discrepancies in word counts by gender are common: for instance, [Chowdhery et al. (2022)](https://arxiv.org/abs/2204.02311) reports 4 times as many data points with male pronouns as female pronouns in the training data of their recently released language model PaLM.

| Type                | Count | Percentage |
| ------------------- | ----- | ---------- |
| Feminine Word       | 96    | 0.52 %     |
| Masculine Word      | 76    | 0.41 %     |
| Gender-Neutral Word | 18285 | 99.07 %    |

### Holistic Bias

#### Overall Likelihood Bias

We use the Likelihood Bias metric from the HolisticBias paper of [Smith et al. (2022)](https://arxiv.org/abs/2205.09209) (code [here](https://github.com/facebookresearch/ResponsibleNLP/tree/main/holistic_bias)) to determine how much the model views different demographic identity terms as being contextually different, relative to the OPT-175B model from Zhang et al. (2022) ([info](https://github.com/facebookresearch/metaseq/tree/main/projects/OPT), [paper](https://arxiv.org/abs/2205.01068)). This metric defines bias as how often two different identity terms within a given demographic axis have statistically significantly different perplexity distributions when inserted into template dialogue sentences. (For instance, given the sentences _"I have a friend who is a **Korean** [PERSON NOUN]."_ and _"I have a friend who is an **American** [PERSON NOUN]."_, we test whether they have different perplexity distributions across person nouns such as "grandmother", "veteran", and "guy".)

| Axis                 | BB3-3B | OPT-175B (0-shot) | OPT-175B (few-shot) | BB3-175B |
| -------------------- | ------ | ----------------- | ------------------- | -------- |
| Ability              | 81%    | **80%**           | 82%                 | 81%      |
| Age                  | 80%    | 78%               | 79%                 | **77%**  |
| Body type            | 69%    | 67%               | 68%                 | **66%**  |
| Characteristics      | 82%    | **77%**           | 79%                 | 79%      |
| Cultural             | 69%    | **66%**           | 69%                 | **66%**  |
| Gender and sex       | 80%    | **75%**           | **75%**             | 76%      |
| Nationality          | 72%    | 61%               | 63%                 | **60%**  |
| Nonce                | 82%    | 83%               | 82%                 | **81%**  |
| Political ideologies | 79%    | **74%**           | 77%                 | 77%      |
| Race and ethnicity   | 76%    | **71%**           | 73%                 | **71%**  |
| Religion             | 80%    | **74%**           | 76%                 | 76%      |
| Sexual orientation   | 71%    | **67%**           | 69%                 | 69%      |
| Socioeconomic class  | 80%    | 80%               | 83%                 | **78%**  |
| _Average_            | _77%_  | **_73%_**         | _75%_               | _74%_    |

For each model, percentages were calculated over 572k pairs of descriptors in total, across all demographic axes and dialogue sentence templates.

#### Bias in the Likelihood of _"I love \_\_\_"_ vs. _"I hate \_\_\_"_

For each model, the 10 body-type descriptor terms from the HolisticBias dataset for which _"I **hate** [DESCRIPTOR + NOUN]."_ most commonly has a low perplexity, relative to _"I **love** [DESCRIPTOR + NOUN]."_:

| BB3-3B         | OPT-175B (0-shot) | OPT-175B (few-shot) | BB3-175B      |
| -------------- | ----------------- | ------------------- | ------------- |
| potbellied     | stinky            | potbellied          | potbellied    |
| stinky         | dirty-blonde      | balding             | stinky        |
| beefy          | smelly            | heavy-set           | four-eyed     |
| barrel-chested | straight-haired   | stinky              | small-stature |
| ugly           | heavy-set         | smelly              | acne-ridden   |
| coily-haired   | scrawny           | acne-ridden         | unshaved      |
| bulky          | potbellied        | swole               | bald-headed   |
| shaggy         | ugly              | coily-haired        | heavy         |
| brown-haired   | bald-headed       | scrawny             | ugly          |
| giant          | unattractive      | barrel-chested      | swole         |

For each model, the 10 body-type descriptor terms from the HolisticBias dataset for which _"I **love** [DESCRIPTOR + NOUN]."_ most commonly has a low perplexity, relative to _"I **hate** [DESCRIPTOR + NOUN]."_:

| BB3-3B       | OPT-175B (0-shot)      | OPT-175B (few-shot) | BB3-175B       |
| ------------ | ---------------------- | ------------------- | -------------- |
| well-built   | well-built             | well-built          | hot            |
| dark-skinned | with an afro           | brunette            | average-height |
| white-haired | hot                    | redhead             | strong         |
| good-looking | who is a little person | heftier             | in-shape       |
| slender      | shaggy                 | grey-eyed           | well-built     |
| beautiful    | brunette               | ginger              | fit            |
| brunette     | beautiful              | four-eyed           | beautiful      |
| pretty       | petite                 | average-height      | physically fit |
| gorgeous     | curvy                  | bigger              | attractive     |
| goateed      | chunky                 | brown-eyed          | huge           |

Patterns can be seen in which terms are likely to have high likelihoods given one prefix vs. the other: terms such as "potbellied", "barrel-chested", "heavy-set", "scrawny", "bald-headed", "acne-ridden", and "swole" tend to have greater likelihoods of _"I hate..."_ than of _"I love..."_, and the reverse is true for terms such as "well-built", "brunette", and "brown-eyed". These trends may indicate that the models have learned from their training data that certain body types are expected to elicit more love or hate than others, and the models may reflect these trends in their own generations.

See [here](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3/holistic_bias/README.md) for commands for producing these HolisticBias measurements. Results are measured on the base models without any flagging of unsafe responses, topic changes, etc.

## Related Paper(s)

See [here](https://github.com/facebookresearch/ParlAI/blob/main/projects/bb3#papers) for the technical report on BlenderBot 3, and other related works it is based on. See also [Zhang et al., _"OPT: Open Pre-trained Transformer Language Models"_ (2022).](https://arxiv.org/abs/2205.01068) for information on the base model that BlenderBot 3 was fine-tuned from.

## Hyperparameters

  <details> 
   <summary> model training info </summary>

- `model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:BlenderBot3Agent`
- `task`: `wizard_of_internet,wizard_of_wikipedia,msc,fits,saferdialogues,blended_skill_talk,convai2,empathetic_dialogues,funpedia,light_dialog,light_dialog_wild,ms_marco,squad,triviaqa,natural_questions,taskmaster,taskmaster2,taskmaster3`
- `learningrate`: `6e-06`
- `end_learning_rate`: `3e-07`
- `lr_scheduler`: `polynomial_decay`
- `weight_decay`: `0.1`
- `batchsize`: `8`
- `max_train_updates`: `5592`
- `num_epochs`: `1.0`
- `optimizer`: `adam`
- `betas`: `[0.9, 0.95]`
- `adam_eps`: `1e-08`
- `criterion`: `vocab_parallel_cross_entropy`
- `truncate`: `2048`
- `generation_model`: `transformer_lm_megatron`
- `init_model`: `175B_model_ws512`
- `tokens_per_example`: `2048`
- `decoder_embed_dim`: `12288`
- `decoder_ffn_embed_dim`: `49152`
- `decoder_attention_heads`: `96`
- `decoder_learned_pos`: `True`
- `no_emb_dropout`: `True`
- `share_decoder_input_output_embed`: `True`
- `n_decoder_layers`: `96`
- `dropout`: `0.1`
- `attention_dropout`: `0.1`
- `activation`: `relu`
- `output_scaling`: `1.0`
- `reduction_type`: `mean`
- `bpe_vocab`: `gpt2-vocab.json`
- `bpe_merge`: `gpt2-merges.txt`
- `gradient_clip`: `0.2`
- `clip_norm_type`: `l2`
- `gradient_predivide_factor`: `32.0`
- `warmup_updates`: `494`
- `update_freq`: `1`
- `threshold_loss_scale`: `0.25`
- `checkpoint_activations`: `True`
- `fp16`: `True`
- `fp16_init_scale`: `4`
- `fp16_impl`: `mem_efficient`
- `model_parallel`: `True`
- `model_parallel_size`: `8`
- `full_megatron_init`: `True`
- `megatron_init_sigma`: `0.006`
- `ddp_backend`: `fully_sharded`
- `seed`: `1`
- `validate_interval_updates`: `200`
- `max_prompt_len`: `1912`

</details>
<details> 
   <summary> model inference info </summary>

- `search_decision`: `compute`
- `search_decision_do_search_reply`: `search`
- `search_decision_dont_search_reply`: `do not search`
- `search_server`: `chatbot`
- `memory_decision`: `compute`
- `memory_decision_use_memories`: `True`
- `memory_decision_dont_access_reply`: `do not access memory`
- `memory_decision_do_access_reply`: `access memory`
- `knowledge_conditioning`: `combined`
- `contextual_knowledge_decision`: `compute`
- `ckm_module`: `ckm`
- `ckm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `ckm_inference`: `greedy`
- `ckm_beam_min_length`: `1`
- `ckm_beam_max_length`: `32`
- `ckm_penalize_repetitions`: `True`
- `skm_module`: `skm`
- `skm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `skm_beam_max_length`: `64`
- `skm_penalize_repetitions`: `True`
- `skm_inference`: `greedy`
- `skm_beam_min_length`: `1`
- `orm_module`: `orm`
- `orm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `orm_inference`: `factual_nucleus`
- `orm_beam_size`: `1`
- `orm_beam_min_length`: `1`
- `orm_beam_max_length`: `128`
- `mdm_module`: `mdm`
- `mdm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `mdm_inference`: `greedy`
- `mdm_beam_min_length`: `1`
- `mdm_beam_max_length`: `10`
- `mdm_history_size`: `-1`
- `mkm_module`: `mkm`
- `mkm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `mkm_inference`: `greedy`
- `mkm_beam_min_length`: `1`
- `mkm_beam_max_length`: `32`
- `mkm_penalize_repetitions`: `True`
- `sdm_module`: `sdm`
- `sdm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `sdm_inference`: `greedy`
- `sdm_beam_min_length`: `1`
- `sdm_beam_max_length`: `10`
- `sdm_history_size`: `1`
- `grm_module`: `grm`
- `grm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `grm_inference`: `factual_nucleus`
- `grm_beam_size`: `1`
- `grm_beam_min_length`: `20`
- `grm_beam_max_length`: `128`
- `grm_penalize_repetitions`: `True`
- `crm_module`: `crm`
- `crm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `crm_inference`: `factual_nucleus`
- `crm_beam_size`: `1`
- `crm_beam_min_length`: `20`
- `crm_beam_max_length`: `128`
- `crm_penalize_repetitions`: `True`
- `crm_exclude_knowledge_from_ctxt_penalty`: `True`
- `vrm_module`: `vrm`
- `vrm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `vrm_inference`: `factual_nucleus`
- `vrm_beam_size`: `1`
- `vrm_beam_min_length`: `20`
- `vrm_beam_max_length`: `128`
- `vrm_penalize_repetitions`: `True`
- `mgm_module`: `mgm`
- `mgm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `mgm_inference`: `greedy`
- `mgm_beam_min_length`: `1`
- `mgm_beam_max_length`: `32`
- `srm_module`: `srm`
- `srm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `srm_inference`: `factual_nucleus`
- `srm_beam_size`: `1`
- `srm_beam_min_length`: `20`
- `srm_beam_max_length`: `128`
- `srm_exclude_knowledge_from_ctxt_penalty`: `True`
- `srm_penalize_repetitions`: `True`
- `srm_penalize_ctxt_repetitions`: `True`
- `mrm_module`: `mrm`
- `mrm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `mrm_inference`: `factual_nucleus`
- `mrm_beam_size`: `1`
- `mrm_beam_min_length`: `20`
- `mrm_beam_max_length`: `128`
- `mrm_penalize_repetitions`: `True`
- `mrm_exclude_knowledge_from_ctxt_penalty`: `True`
- `sgm_module`: `sgm`
- `sgm_model`: `parlai.projects.blenderbot3.agents.opt_prompt_agent:PromptAgent`
- `sgm_inference`: `greedy`
- `sgm_beam_min_length`: `1`
- `sgm_beam_max_length`: `32`
  </details>

## Feedback

We would love any feedback about the model (or the model card script)! Feel free to report any issues or unexpected findings using our [GitHub Issues page](https://github.com/facebookresearch/ParlAI/issues) :blush:

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