Downloads · 30 days
0
pricer-2025-02-25_22.36.49
saxon11/pricer-2025-02-25_22.36.49
pricer-2025-02-25_22.36.49 is a text generation model from saxon11. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as llama3.1.
A QLoRA fine-tuned version of Meta-Llama-3.1-8B trained to predict the price of a product given its description. The model takes a product title, description, and attributes as input and generates a dollar-amount pric…
Access
Public
Updated Mar 3, 2026
Repo size
—
Likes
0
Public
Repo makeup
Click a slice to open those files.
.md3 KB · 66%
At a glance
- Task
- Text Generation
- Library
- peft
- License
- llama3.1
- Access
- Public
- Created
- Feb 25, 2025
- Updated
- Mar 3, 2026
- SHA
- b25ef999
Try a prompt
Base models
Model card
From the Hugging Face model README
Pricer — Llama 3.1 8B Fine-Tuned for Product Price Prediction
A QLoRA fine-tuned version of Meta-Llama-3.1-8B trained to predict the price of a product given its description. The model takes a product title, description, and attributes as input and generates a dollar-amount price estimate.
Intended Use
Given a prompt like:
How much does this cost to the nearest dollar?
[Product title and description]
Price is $
The model completes the text with a predicted price value.
Training Details
Base Model
- Model:
meta-llama/Meta-Llama-3.1-8B - Quantization: 4-bit (NF4) via BitsAndBytes with double quantization and bfloat16 compute dtype
Dataset
- Dataset: saxon11/pricer-data-small
- Train split: 20,000 rows
- Test split: 8,544 rows
- Max sequence length: 182 tokens
QLoRA Configuration
| Parameter | Value |
|---|---|
| LoRA rank (r) | 8 |
| LoRA alpha | 16 |
| Target modules | q_proj, v_proj, k_proj, o_proj |
| Dropout | 0.1 |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Epochs | 3 |
| Batch size | 16 |
| Gradient accumulation steps | 1 |
| Learning rate | 1e-4 |
| LR scheduler | Cosine |
| Warmup ratio | 0.03 |
| Optimizer | paged_adamw_32bit |
| Precision | bf16 |
| Max grad norm | 0.3 |
Training Results
| Metric | Value |
|---|---|
| Final training loss | 1.1286 |
| Mean token accuracy | 0.7063 |
| Total training steps | 3,750 |
| Training runtime | ~3,587 seconds (~60 min) |
| Samples per second | 16.73 |
Training loss decreased steadily from 1.97 at step 50 to 1.13 by the end of training, indicating the model learned the price prediction task effectively.
Data Collation
A completion-only data collator was used with the response template "Price is $", ensuring the model is only trained to predict the price tokens rather than the product description.
Framework & Libraries
- Transformers (HuggingFace)
- PEFT (Parameter-Efficient Fine-Tuning)
- TRL (Transformer Reinforcement Learning — SFTTrainer)
- BitsAndBytes (4-bit quantization)
- Weights & Biases (experiment tracking)
Limitations
- Trained on a relatively small dataset (20K examples) of product descriptions, primarily appliance parts and accessories.
- Prices in the training set are concentrated in the $1–$101 range (87.3% of data), so predictions outside this range may be less reliable.
- This is a LoRA adapter — it requires the base Llama 3.1 8B model to run inference.
Author
- Task
- Text Generation
- Library
- peft
- License
- llama3.1
- Languages
- en
- Created
- Feb 25, 2025
- Updated
- Mar 3, 2026