Replace auto-generated template with a real model card (dataset, LoRA/QLoRA config, training hyperparameters); document that no retrieval system or quantitative eval exists

README.md

@@ -1,199 +1,124 @@
 ---
-library_name: transformers
-tags: []
+library_name: peft
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+license: mit
+tags:
+- lora
+- qlora
+- peft
+- supervised-fine-tuning
+- finance
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Vault-8b-Instruct
 
+A QLoRA fine-tune of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) for answering questions about financial 10-K filings, given the relevant passage as context.
 
 ## 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]
+Vault-8b-Instruct is Llama-3-8B-Instruct fine-tuned with 4-bit QLoRA on 4,000 examples built from [virattt/financial-qa-10K](https://huggingface.co/datasets/virattt/financial-qa-10K): each example pairs a question about a 10-K filing with the specific passage that answers it, and the model is trained to answer using only that provided passage.
 
-### Model Sources [optional]
+- **Developed by:** [harshil30402](https://huggingface.co/harshil30402)
+- **Model type:** Causal decoder-only LM, LoRA adapter over Llama-3-8B-Instruct
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/harshil30402/Vault-8b-Instruct
+- **Training dataset:** https://huggingface.co/datasets/virattt/financial-qa-10K
 
 ## 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]
+Answering a question about a financial filing when the relevant passage is supplied directly in the prompt (closed-book, context-conditioned QA) — e.g. as the answer-generation step in a larger document QA system that has already located the relevant passage some other way.
 
 ### 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]
+**This model does not perform retrieval.** It was not trained or evaluated as part of a retrieval-augmented generation (RAG) pipeline — there is no embedding/vector-search/chunking component. If you need to find the relevant passage in a large filing before answering, that retrieval step has to be built separately; this model only handles the answer-generation half, given the passage already in hand.
 
 ## 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]
+- 4,000 training / 500 validation / 100 test examples, each a `(question, context, answer)` triple built from real 10-K filing excerpts (e.g. segment breakdowns, net earnings figures, loan interest rates).
+- Formatted with the Llama-3 chat template and a fixed system prompt instructing the model to answer using only the given passage ("Use only the information to answer the question").
 
 ### 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]
-
+QLoRA: 4-bit-quantized base model with a LoRA adapter trained on top.
 
 #### 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]
+| Hyperparameter | Value |
+|---|---|
+| LoRA rank (r) | 32 |
+| LoRA alpha | 16 |
+| LoRA dropout | 0.005 |
+| LoRA target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| LoRA bias | none |
+| Learning rate | 1e-4 |
+| LR scheduler | constant, 10% warmup |
+| Optimizer | paged AdamW 8-bit |
+| Effective batch size | 8 (per-device 2 × gradient accumulation 4) |
+| Epochs | 1 |
+| Precision | fp16 |
+| Weight decay | 0.0 |
+| Max sequence length | 512 |
+| Seed | 42 |
 
 ## 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. -->
+**Not available.** No retrieval-quality or answer-quality evaluation was run for this model — there is no before/after accuracy comparison, and no benchmark score to report. An earlier, unpublished claim that this fine-tune "reduced VRAM usage by ~60%" could not be traced to any actual memory measurement and is not repeated here; if you need a real efficiency number, it would need to be measured fresh (e.g. comparing peak memory for 4-bit-quantized vs. full-precision inference on the same hardware).
 
-[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]
+## How to Get Started with the Model
 
-<!-- Relevant interpretability work for the model goes here -->
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
 
-[More Information Needed]
+base_model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
+adapter_name = "harshil30402/Vault-8b-Instruct"
 
-## Environmental Impact
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+base_model = AutoModelForCausalLM.from_pretrained(base_model_name, quantization_config=bnb_config, device_map="auto")
+model = PeftModel.from_pretrained(base_model, adapter_name)
+model.eval()
 
-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).
+question = "How much did the company's net earnings amount to in fiscal 2022?"
+context = "Net earnings were $17.1 billion in fiscal 2022."
+messages = [
+    {"role": "system", "content": "Use only the information to answer the question"},
+    {"role": "user", "content": f"{question}\n\nInformation:\n\n```\n{context}\n```"},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
 
-- **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]
+Requires a CUDA GPU for 4-bit quantized loading (`bitsandbytes`).
 
-## Technical Specifications [optional]
+## Bias, Risks, and Limitations
 
-### Model Architecture and Objective
+- No quantitative evaluation exists for this model — accuracy, faithfulness to the provided context, and hallucination rate are all unmeasured.
+- Trained on general 10-K excerpts across unspecified companies/sectors; not validated on any specific company's filings or regulatory context.
+- Not a retrieval system — cannot locate relevant passages in a full filing on its own.
 
-[More Information Needed]
+## Technical Specifications
 
 ### 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]
+- **Software:** PyTorch, Transformers, PEFT, bitsandbytes (4-bit QLoRA).
 
 ## Model Card Contact
 
-[More Information Needed]
+harshil30402