khazarai
/

Personal-Finance-R2

@@ -1,199 +1,111 @@
 ---
 library_name: transformers
-tags: []
 ---
 # 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.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## 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. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
 Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### 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
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## 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. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+tags:
+- unsloth
+- sft
+- reasoning
+license: apache-2.0
+datasets:
+- Akhil-Theerthala/Kuvera-PersonalFinance-V2.1
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
 ---
 # Model Card for Model ID
+This model is fine-tuned for instruction-following in the domain of personal finance, with a focus on:
+- Budgeting advice
+- Investment strategies
+- Credit management
+- Retirement planning
+- Insurance and financial planning concepts
+- Personalized financial reasoning
 ### Model Description
+- **License:** MIT
+- **Finetuned from model:** unsloth/Qwen3-1.7B
+- **Dataset:** The model was fine-tuned on the Kuvera-PersonalFinance-V2.1, curated and published by Akhil-Theerthala.
+###  Model Capabilities
+- Understands and provides contextual financial advice based on user queries.
+- Responds in a chat-like conversational format.
+- Trained to follow multi-turn instructions and deliver clear, structured, and accurate financial reasoning.
+- Generalizes well to novel personal finance questions and explanations.
 ## Uses
 ### Direct Use
+- Chatbots for personal finance
+- Educational assistants for financial literacy
+- Decision support for simple financial planning
+- Interactive personal finance Q&A systems
 ## Bias, Risks, and Limitations
+- Not a substitute for licensed financial advisors.
+- The model's advice is based on training data and may not reflect region-specific laws, regulations, or financial products.
+- May occasionally hallucinate or give generic responses in ambiguous scenarios.
+- Assumes user input is well-formed and relevant to personal finance.
 ## How to Get Started with the Model
 Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("khazarai/Personal-Finance-R2")
+model = AutoModelForCausalLM.from_pretrained(
+    "khazarai/Personal-Finance-R2",
+    device_map={"": 0}
+)
+question = """ I just got accepted into Flatiron's full-time software engineering bootcamp, but I have basically no savings and the $19k price tag is freaking me out.
+I really love coding and want to break into tech, but I'm looking at taking out a loan through Climb or Ascent with around 6.5% interest—that'd mean paying like $600 a month after.
+Is this a smart move? I'm torn between chasing this opportunity and being terrified of the debt. Any advice?
+"""
+messages = [
+    {"role" : "user", "content" : question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+    enable_thinking = True,
+)
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 3000,
+    temperature = 0.6,
+    top_p = 0.95,
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
 ## Training Details
 ### Training Data
+- Dataset Overview:
+  Kuvera-PersonalFinance-V2.1 is a collection of high-quality instruction-response pairs focused on personal finance topics.
+  It covers a wide range of subjects including budgeting, saving, investing, credit management, retirement planning, insurance, and financial literacy.
+- Data Format:
+  The dataset consists of conversational-style prompts paired with detailed and well-structured responses.
+  It is formatted to enable instruction-following language models to understand and generate coherent financial advice and reasoning.