Raw regulatory filing or report
AI Models & Assistants / Knowledge library
finance-chat
Older 7B finance-domain research baseline; not validated for investment decisions.
Why learn finance-chat?
Provides downloadable model weights for controlled, task-specific evaluation.
The problem it solves
Supports local model experimentation; category recommendations are editorial, not measured rankings.
Where it came from
finance-chat: Older 7B finance-domain research baseline; not validated for investment decisions. License: Llama 2 Community License. Source checked 2026-10-13. Evaluate on your own task before use.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what finance-chat does.
- Identify a use case for finance-chat.
- Describe a limitation of finance-chat.
- Name a compatible tool for finance-chat.
- Compare finance-chat against an alternative.
Documentation and sources
- Primary source / model card (huggingface.co)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Corporate Disclosure Query Assistant
Financial analysts and compliance officers reviewing large volumes of regulatory filings.
The business problem
Generic LLMs often struggle to accurately extract or synthesize information from dense, domain-specific financial documents, leading to hallucinations or missed details in critical reports.
How this topic solves it
The AdaptLLM finance-chat model utilizes continued pre-training on domain-specific corpora, structured as reading comprehension tasks, to improve performance on specialized financial datasets while maintaining conversational capabilities.
- Load the pre-trained AdaptLLM finance-chat model using the transformers library.
- Initialize the tokenizer associated with the model to handle input text.
- Format the user's financial query into the required LLaMA-2-Chat multi-turn conversation structure.
- Inject the target financial document as context within the system prompt.
- Execute the generation process to retrieve a domain-grounded response.
The flow of data from raw financial documents to the final user-facing response.
LLaMA-2-Chat format integration
Domain-adapted 7B model
Extracted financial insight
- Financial Document → context injection → Prompt Template
- Prompt Template → tokenized input → Finance-Chat Model
- Finance-Chat Model → generation → Response
Implementation example
This code demonstrates loading the finance-chat model and formatting a query using the required system prompt structure for chat-aligned models.
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the domain-adapted model
model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat")
# Apply system prompt and user input in LLaMA-2-Chat format
prompt = f"<s>[INST] <<SYS>> {system_prompt} <</SYS>>\n\n {user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.input_ids, max_length=4096)Expected business outcome
Improved accuracy in extracting specific financial data points from complex regulatory filings compared to general-purpose models.
Limitations and checks
The model is a 7B parameter research baseline and is not validated for investment decisions. Users should verify outputs against primary source documents to ensure factual accuracy.