Update README.md

README.md

@@ -1,11 +1,19 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- finance
+license: mit
+datasets:
+- Recompense/amazon-appliances-lite-data
+language:
+- en
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+Predicts Prices based on product description.
 
 
 
@@ -13,187 +21,170 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model predicts prices of amazon aplliances data based on a product description
 
-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]
+- **Developed by:** [https://huggingface.co/Recompense]
+- **Model type:**  Transformer (causal, autoregressive)
+- **Language(s) (NLP):** English
+- **License:** [MIT]
+- **Finetuned from model:** meta-llama/Llama-3.1-8B-Instruct
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://huggingface.co/Recompense/Midas-pricer/]
 
 ## 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
+- Primary use case: Generating estimated retail prices for household appliances from textual descriptions.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- Example applications:
 
-[More Information Needed]
+  Assisting e-commerce teams in setting competitive price points
 
-### Downstream Use [optional]
+  Supporting market analysis dashboards with on-the-fly price estimates
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+- Not intended for: Financial advice or investment decisions
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- Attempting to predict prices outside the appliances domain (e.g., electronics, furniture, vehicles) will likely yield unreliable results.
 
-[More Information Needed]
+- Using this model for any price-sensitive or regulatory decisions without human oversight is discouraged.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- Data biases: The training dataset is drawn exclusively from Amazon appliance listings. Price distributions are skewed toward mid-range consumer electronics; extreme low or high‐end appliances are underrepresented.
+
+- Input sensitivity: Minor changes in phrasing or additional noisy tokens can shift predictions noticeably.
+
+- Generalization: The model does not understand supply chain disruptions, seasonality, or promotions—it only captures patterns seen in historical listing data.
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Always validate model outputs against a small set of ground-truth prices before production deployment.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Use this model as an assistant, not an oracle: incorporate downstream business rules or domain expertise.
 
-## How to Get Started with the Model
+- Regularly retrain or fine-tune on updated listing data to capture shifting market trends.
 
-Use the code below to get started with the model.
+## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-[More Information Needed]
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Recompense/Midas-pricer")
+model = AutoModelForCausalLM.from_pretrained("Recompense/Midas-pricer", torch_dtype=torch.bfloat16)
 
-## Training Details
+# Prepare prompt
+product_desc = "How much does this cost to the nearest dollar?\n\nSamsung 7kg top-load washing machine with digital inverter motor"
+prompt = f"{product_desc}\n\nPrice is $"
 
-### Training Data
+# Tokenize and generate
+inputs = tokenizer(prompt, return_tensors="pt")
+attention_mask = torch.ones(inputs.shape, device="cuda")
+generated = model.generate(inputs, attention_mask=attention_mask, max_new_tokens=3, num_return_sequences=1)
+price_text = tokenizer.decode(generated[0], skip_special_tokens=True)
 
-<!-- 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. -->
+print(f"Estimated price: ${price_text}")
+```
 
-[More Information Needed]
 
-### Training Procedure
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+- Dataset: Recompense/amazon-appliances-lite-data
 
-[More Information Needed]
+- Train/validation/test split: 80/10/10
 
+### Training Procedure
 
 #### 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 -->
+- Fine-tuning framework: PyTorch + Hugging Face Accelerate
+
+- Precision: bf16 mixed precision
 
-#### Speeds, Sizes, Times [optional]
+- Batch size: 32 sequences
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- Learning rate: 1e-5 with linear warmup (10% of total steps)
 
-[More Information Needed]
+- Optimizer: AdamW
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
 ### Testing Data, Factors & Metrics
+- Test set: Held-out 10% of listings (≈5 000 examples)
 
-#### 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. -->
+- Metric: Root Mean Squared Logarithmic Error (RMSLE)
 
-[More Information Needed]
+- Hit@$40: Percentage of predictions within ±$40 of true price
 
-#### Metrics
+| Metric   | Value  |
+| -------- | ------ |
+| RMSLE    | 0.61   |
+| Hit@\$40 | 85.2 % |
 
-<!-- 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]
+The model achieves an RMSLE of 0.61, indicating good alignment between predicted and actual prices on a log scale,
+and correctly estimates within $40 in over 85% of test cases.
+This performance is competitive for rapid prototyping in price-sensitive applications.
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+- Approximate compute emissions for fine-tuning (using ML CO₂ impact calculator):
 
-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: Tesla T4
 
-- **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]
+- Duration: 2 hours(0.06 epochs)
 
-## Technical Specifications [optional]
+- Cloud provider: Google Cloud, region US-Central
 
-### Model Architecture and Objective
+- Estimated CO₂ emitted: 6 kg CO₂e
 
-[More Information Needed]
+## Technical Specifications
+## Model Architecture
+- Base model: Llama-3.1-8B (8 billion parameters)
 
-### Compute Infrastructure
+- Objective: Autoregressive language modeling with instruction tuning
 
-[More Information Needed]
+## Compute Infrastructure
+- Hardware: 4× Tesla T4 GPUs
 
-#### Hardware
+- Software:
 
-[More Information Needed]
+  PyTorch 2.x
 
-#### Software
+  transformers 5.x
 
-[More Information Needed]
+  accelerate 1.x
 
-## 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]
+  bitsandbytes (for 8-bit quantization optional inference)
 
-**APA:**
+## Citation [optional]
+@misc{RCompense2025MidasPricer,
+  title        = {Midas-Pricer: Price Prediction for Amazon Appliances},
+  author       = {Recompense},
+  year         = {2025},
+  howpublished = {\url{https://huggingface.co/Recompense/Midas-pricer}},
+}
 
-[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]
+- RMSLE (Root Mean Squared Logarithmic Error):
+Measures the square root of the average squared difference between log-transformed predictions and targets. Less sensitive to large absolute errors.
 
-## Model Card Authors [optional]
+- Hit@$40:
+  Fraction of predictions whose absolute error is ≤ $40.
 
-[More Information Needed]
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
+Damola Jimoh(Recompense)