LongShort-Dolly-2-7B
Model Description
LongShort-Dolly-2-7B is a large language model fine-tuned on earnings call documents to extract financial KPIs from the earnings call documents. It is based on the Dolly-2-7B Architecture.
- Model creator: Brief AI
- Original model: Dolly-2-7B
Dataset Description
- Data Source: Factiva
- Data Description: 28K+ Earnings Call Documents
- Data Scope: 1K+ public companies
- Fine Tuning Data: Collection of 60K+ samples.
Prompt template: LongShort-Dolly-2-7B
[INST]Given the context, answer the question.
### Question:
Extract all the finance-based performance indicators and evaluation metrics.
### Context:
{context}
### Answer:
[/INST]
Basics
This section provides information about the model type, version, license, funders, release date, developers, and contact information. It is useful for anyone who wants to reference the model.
Developed by: Brief AI Team
Model Type: Transformer-based Large Language Model
Version: 1.0.0
Languages: English
License: Apache 2.0
Release Date Estimate: Wednesday, 29.November.2023
Send Questions to: vishalparameswaran96@gmail.com
Cite as: Brief AI LongShort Language Model
Funded by: UChicago Data Science Institute
Mentored by: Nick Kadochnikov
Technical Specifications
This section includes details about the model objective and architecture, and the compute infrastructure. It is useful for people interested in model development.
Please see the LongShort training README for full details on replicating training.
Model Architecture and Objective
- Modified from Dolly-2-7B
Objective: Financial KPI extraction from earnings call documents.
Hardware and Software - Compute Infrastructure
4 NVIDIA L4 GPUs & 48 vCPUs
Environment: PyTorch (pytorch-2.0 w/ CUDA-11.8; see Github link)
CPU: GCP G2 Standard 48 (Platform: Intel Cascade Lake) (Accelerator Optimized)
CPU memory: 192GB RAM
GPU memory: 30GB per GPU
Training
This section provides information about the training. It is useful for people who want to learn more about the model inputs and training footprint.
The following bits and bytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
Framework versions
- PEFT 0.4.0
Training Data
This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.
Details for the dataset can be found in LongShort Dataset
Training data includes:
- 5000 Earnings Call Documents
How to use
This model can be easily used and deployed using HuggingFace's ecosystem. This needs transformers and accelerate installed. The model can be downloaded as follows:
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 pre-trained base model that can be further fine-tuned for specific tasks. The 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
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 may not be correct.
Out-of-scope Uses Include:
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
Unconsented impersonation and imitation
Unconsented surveillance
Generating content without attribution to the model, as specified in the RAIL License, Use Restrictions
Intended Users
Direct Users
General Public
Researchers
Students
Educators
Engineers/developers
Non-commercial entities
Financial Industry
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
Induce users into attributing human traits to it, such as sentience or consciousness
Evaluation
This section describes the evaluation protocols and provides the results.
Result: LongShort-Falcon-7B gives 45.4% accuracy on a validation set of 10% of the original training dataset.
Train-time Evaluation:
Final checkpoint after 700 epochs:
- Training Loss: 1.645
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.
Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
Model Card Authors
Vishal Parameshwaran, Garima Sohi, Jose Gerala, Sanchit Narayan Kumar
- Downloads last month
- 0