Rardilit/Panther_v1

Panther

Rardilit Large Open-access Language Model

Model Card

Version 1.0 / 29.May.2023

Model Card for Bloom-560m

Table of Contents

1. Model Details
2. Uses
3. Bias, Risks, and Limitations
4. Recommendations
5. Training Details

Model Details

Model Description

This section provides information for anyone who wants to know about the model.

• Developed by: Rardilit (website)

  • All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)

• Model Type: Transformer-based Language Model

• Version: 1.0.0

• Languages: Multiple;

• License: Panther License v1.0 (link)

• Release Date Estimate: Monday, 16.May.2023

Uses

This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. It provides information for anyone considering using the model or who is affected by the model.

Intended Use

This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.

Direct Use

• Text generation

• Exploring characteristics of language generated by a language model

  • Examples: Cloze tests, counterfactuals, generations with reframings

Downstream Use

• Tasks that leverage language models include: Information Extraction, Question Answering, Summarization

Misuse and Out-of-scope Use

This section addresses what users ought not do with the model.

Out-of-scope Uses

Using the model in high-stakes settings is out of scope for this model.  The model is not designed for critical decisions nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.

Out-of-scope Uses Include:

• Usage in biomedical domains, political and legal domains, or finance domains

• Usage for evaluating or scoring individuals, such as for employment, education, or credit

• Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct

Misuse

Intentionally using the model for harm, violating human rights, or other kinds of malicious activities, is a misuse of this model. This includes:

• Spam generation

• Disinformation and influence operations

• Disparagement and defamation

• Harassment and abuse

• Deception

• Unconsented impersonation and imitation

• Unconsented surveillance

• Generating content without attribution to the model

Intended Users

Direct Users

• General Public

• Researchers

• Students

• Educators

• Engineers/developers

• Non-commercial entities

• Community advocates, including human and civil rights groups

Indirect Users

• Users of derivatives created by Direct Users, such as those using software with an intended use

Others Affected (Parties Prenantes)

• People and groups referred to by the LLM

• People and groups exposed to outputs of, or decisions based on, the LLM

• People and groups whose original work is included in the LLM

Bias, Risks and Limitations

This section identifies foreseeable harms and misunderstandings.

Model may:

• Overrepresent some viewpoints and underrepresent others

• Contain stereotypes

• Contain personal information

• Generate:

  • Hateful, abusive, or violent language

  • Discriminatory or prejudicial language

  • Content that may not be appropriate for all settings, including sexual content

• Make errors, including producing incorrect information as if it were factual

• Generate irrelevant or repetitive outputs

Recommendations

This section provides information on warnings and potential mitigations.

• Indirect users should be made aware when the content they're working with is created by the LLM.

• Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary.

• Models pretrained with the LLM should include an updated Model Card.

• Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.

Training Details

This repo contains a low-rank adapter for LLaMA-7b with just 4194304 parameters fit on the Rardilit/Panther-dataset_v1 dataset with 20k prompts and responses.

This version of the weights was trained with the following hyperparameters:

• Epochs: 1 (load from best epoch)

• LORA_R = 8

• LORA_ALPHA = 16

• LORA_DROPOUT= 0.05

• LORA_TARGET_MODULES = [ "q_proj", "v_proj", ]

• BATCH_SIZE = 300

• MICRO_BATCH_SIZE = 4

• GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE

• LEARNING_RATE = 3e-4

• TRAIN_STEPS = 10

• warmup_steps = 10

• logging_steps = 1

• fp16 = true

• optim = "adamw_torch"

• eval_steps=4

• save_steps=8

Training Time

The time in training this model with 1 x T4 16gb vRAM was approx. 45 min.