Instructions to use AmPac/trace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AmPac/trace with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("AmPac/trace") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use AmPac/trace with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "AmPac/trace" --prompt "Once upon a time"
- Atomic Chat
Trace
Trace is a specialized MLX LoRA adapter trained on high-quality, curated synthetic data for SBA 504 and 7(a) screening.
It helps a lender identify the rule that applies on a loanโs effective date, surface a potential hard stop, and prepare a reviewable screening summary. A qualified human must confirm every eligibility and credit decision.
What is included
- LoRA adapter weights in MLX format
adapter_config.json- Evaluation visuals based on synthetic screening scenarios
The complete Trace system also uses a dated retrieval layer and deterministic policy checks. Those components are separate from this adapter.
Training and grounding
The adapter was trained on 11,196 curated synthetic examples and 768 screening stems. The examples use fictional entities and are grounded in these authoritative sources:
- SBA SOP 50 10 โ Lender and Development Company Loan Programs
- 13 CFR Part 120 โ Business Loans
- 13 CFR Part 121 โ Small Business Size Regulations
- 13 CFR 120.120 โ Eligible use of proceeds
- 13 CFR 120.131 โ Leasing part of an existing or proposed building
The effective loan date selects the applicable SOP version. The complete system retrieves the source text before producing a screening summary.
Model details
| Field | Value |
|---|---|
| Architecture | Causal language model with LoRA adapter |
| Base model | Qwen2.5-7B-Instruct 4-bit for MLX |
| Fine-tuning method | MLX LoRA, rank 16, prompt masking enabled |
| Selected checkpoint | v9-160 |
| Primary output | Screening JSON and officer-facing draft summary |
| Intended users | Trained lending and compliance staff |
Evaluation
ScreenBench holdout uses 12 frozen synthetic files and a deterministic grader.
| Configuration | All-pass | False-kill |
|---|---|---|
| Adapter without the Trace runtime | 6/12 | 0 |
| Complete system with retrieval and policy checks | 12/12 | 0 |
These results measure the complete screening workflow on a small internal holdout. They are not an independent certification.
Quickstart
from mlx_lm import load, generate
model, tokenizer = load(
"mlx-community/Qwen2.5-7B-Instruct-4bit",
adapter_path="AmPac/trace",
)
The adapter alone does not provide the dated retrieval and deterministic policy checks used by the complete Trace system.
Important limitations
Trace does not determine eligibility, approve credit, or speak for the U.S. Small Business Administration. It produces review drafts for qualified staff.
Do not use Trace as the sole basis for an approval, decline, servicing action, or applicant communication. Confirm the source text and effective date before relying on an output.
License and attribution
Repository license: other. The base model has its own license and terms. SBA SOPs, federal regulations, and notices remain subject to their source terms.
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