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raj-hitpromo
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Model Details
Model Provider
raj-hitpromo
Model Tree
microsoft/Phi-4-mini-reasoning
Input Modalities
Output Modalities
Supported Functionality
Validation loss fell at every checkpoint, and the best checkpoint is the final one -- the model was still improving when the epoch ended, with no sign of overfitting: 0.182 -> 0.128 over 9 checkpoints, with token accuracy rising to 96.5%. Measured on the full held-out validation split at every checkpoint.
| Step | Validation loss | Token accuracy |
|---|---|---|
| 100 | 0.18177 | 95.48% |
| 200 | 0.15792 | 95.93% |
| 300 | 0.14828 | 96.15% |
| 400 | 0.14368 | 96.19% |
| 500 | 0.13787 | 96.31% |
| 600 | 0.13343 | 96.40% |
| 700 | 0.12974 | 96.50% |
| 800 | 0.12855 | 96.51% |
| 887 | 0.12835 | 96.52% |
A QLoRA fine-tune of microsoft/Phi-4-mini-reasoning,
trained on HIT's product feed and knowledge base. The base model is open-weights,
American-built, and reasons before it answers; the adapter teaches it this catalog
and HIT's order policy, not general promotional-products trivia.
Internal support for HIT sales reps and distributors, with catalog records supplied in the prompt. The pipeline retrieves the relevant product rows and policy chunks and puts them in context; the model reasons over them. It is not a catalog lookup on its own, and it is not a substitute for the live feed.
15,779 supervised examples generated from HIT's product feed and knowledge base.
| Split | Examples | Source groups | Mean answer chars |
|---|---|---|---|
| train | 14,201 | 3,570 | 607.9 |
| val | 790 | 188 | 620.9 |
| test | 788 | 208 | 581.0 |
| Task | Examples | What it teaches |
|---|---|---|
quantity_pricing | 4,000 | quantity and budget arithmetic across the price breaks |
product_qa | 4,000 | grounded single-field lookups (imprint area, colors, origin, MOQ) |
spec_extraction | 3,999 | free text to a structured spec JSON |
policy_lookup | 1,666 | lead times, minimums, less-than-minimum, compliance, returns |
Splits are grouped by source_id, not sampled per row: every colour variation of a style moves together, so a held-out score cannot be inflated by having seen the same product in training under a different variation code.
Every example was generated only where the answer is derivable from the source record. Nothing plausible-sounding was filled in: a model trained on invented specs learns to invent specs.
| Setting | Value |
|---|---|
| Base model | microsoft/Phi-4-mini-reasoning |
| Method | QLoRA (frozen nf4 base) |
| LoRA rank / alpha | 32 / 64 |
| LoRA dropout | 0.05 |
| Target modules | qkv_proj, o_proj, gate_up_proj, down_proj |
| Epochs | 1 |
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel = AutoModelForCausalLM.from_pretrained("raj-hitpromo/hit-promo-reasoner", dtype="bfloat16", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("raj-hitpromo/hit-promo-reasoner")
The system prompt matters -- it carries the NET-pricing and grounding rules the
model was trained against. It is in hit_model/data/templates.py as
SYSTEM_PROMPT, and the serving path applies it for you.
Built with the pipeline in this project; see MODEL_BUILD.md for the full
method. Card generated 2026-09-12 from the config, dataset
stats, and evaluation report that produced this artifact.
decoration_advice | 1,079 | which decoration methods a product and its art support |
comparison | 869 | A vs B for a stated use case |
product_recommendation | 128 | a brief in, a ranked shortlist with reasons out |
kb_qa | 38 | grounded Q&A over the knowledge base |
| Learning rate | 0.0001 |
| Effective batch | 16 |
| Max sequence length | 4096 |
| Reasoning format | think_tags |