---
base_model: Qwen/Qwen3.5-4B
library_name: peft
pipeline_tag: text-generation
license: cc-by-nc-4.0
language:
- en
tags:
- manifest
- supply-chain
- domain-specialization
- lora
- peft
- text-generation
model-index:
- name: manifest-orchestrator-4b-v0.6.0
  results:
  - task:
      type: text-generation
      name: Supply-chain expert QA
    dataset:
      name: supply-chain-eval
      type: metafloor-ai/supply-chain-eval
    metrics:
    - type: win-rate
      value: 0.951
      name: Preferred over base model by independent LLM judge panel
---

# Manifest 4B · v0.6.0

**The most capable Manifest model available today — deep reasoning for the hardest, multi-constraint supply-chain problems.**

Manifest is MetaFloor's suite of supply-chain expert models — purpose-built specialists in procurement,
demand planning, warehouse operations, supplier relationship management, risk & resilience,
transportation, inventory, and order fulfilment.

- **Family:** Manifest · **This model:** Manifest 4B (4-billion-parameter base)
- **Tier:** advanced — the most capable model available today

> ### Preferred **95.1%** of the time over the base model
> On 134 held-out expert questions, an independent LLM judge panel picked this model's answer over the
> untuned base model's answer **95.1%** of the time (95% CI 91.0–98.5%). With both models given the same
> answer format, it is still preferred **97.0%** of the time — the gain is real domain knowledge, not just presentation.

## What's new in v0.6

- **Retrained on MetaFloor's expanded ~32k-example supply-chain dataset** (up from ~12.5k in v0.5).
- **The evaluation benchmark grew to 134 held-out questions** (from 116) — so v0.6 headline figures are measured on a larger, harder set than the v0.5 cards.
- **A new [Manifest 9B](https://huggingface.co/metafloor-ai/manifest-orchestrator-9b-v0.6.0) tier is joining the family** (coming soon); the 35B orchestrator is retired from this release.
- This is a **new versioned repo** — the previous v0.5 release ([`metafloor-ai/manifest-orchestrator-4b`](https://huggingface.co/metafloor-ai/manifest-orchestrator-4b), unchanged) keeps its own history and download stats.

**v0.5 → v0.6, same 134-question benchmark (preferred over the untuned base):**

| Release | Preferred over base | vs base + matched format |
|---|---|---|
| v0.5.0 | 88.8% | 90.9% |
| **v0.6.0** | **95.1%** | **97.0%** |

*(The baseline throughout is the untuned `Qwen/Qwen3.5-4B` — every win-rate above is measured against it.)*

## Best for

The heavy end: complex, multi-constraint scenarios where the answer has to be right and reasoned.

- Multi-echelon strategy, network design, and cost-vs-service trade-offs
- Executive-level advisory over messy, real-world operating constraints
- Nuanced supplier, risk, and resilience decisions
- Any task where answer quality matters more than latency or cost

*(Need speed or volume instead? See **Manifest 2B** and **Manifest 0.8B** below.)*

## See the difference

Same question. Base model vs Manifest 4B.

> **Ask** *(COO of a $5.8B enterprise, 176 nodes, mostly manual):* "Explain the core concept of MEIO and why it's relevant given our manual, single-echelon approach."
>
> **Base model** → *"Thinking Process: 1. Analyze the Request: Role: COO of a global enterprise. Company Size: $5,812M revenue. Network: 176 network nodes…"* — narrates its own reasoning instead of answering.
>
> **Manifest 4B** → *"MEIO is the optimization of inventory across multiple echelons of the supply chain at once, rather than optimizing each echelon separately…"* — answers directly, like a practitioner, then ties it to the operation.

## The Manifest family

Two kinds of models:

**🧭 Orchestrators — general-purpose, handle any supply-chain area**

| Model | Size | Preferred over base | Status |
|---|---|---|---|
| Manifest 0.8B | 0.8B | 79.9% | ✅ available |
| Manifest 2B | 2B | 85.1% | ✅ available |
| **Manifest 4B** | 4B | **95.1%** | ✅ available |
| Manifest 9B | 9B | — | 🔜 coming soon |

**🎯 Domain-experts — specialized for a single area**

| Model | Preferred over base | Status |
|---|---|---|
| Manifest Specialist · Risk & Resilience | 100% (20 / 0) | ✅ available |
| Manifest Specialist · Inventory Optimization | 80.0% | ✅ available |
| Manifest Specialist · Demand Planning | 82.5% | ✅ available |

*Orchestrators are scored on the general supply-chain benchmark; domain-experts on their focused domain benchmark (20 items each — treat those exact figures as directional).*

## Not just a model — a full stack

Manifest is backed by everything needed to build it and trust it:

- **A purpose-built dataset** — thousands of supply-chain instruction–response pairs spanning 8 sub-domains and every company scale, generated by a seed-driven *operator-as-teacher* pipeline.
- **A reproducible training pipeline** — documented LoRA fine-tuning.
- **An independent benchmark** — 134 held-out expert questions, scored blind by a panel of LLM judges.

We built the model, the data, and the evaluation.

## How to use

Manifest 4B is a LoRA adapter (~101 MB), applied on top of its base model at load time.

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen3.5-4B"  # base model — see "Built on" below
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, "metafloor-ai/manifest-orchestrator-4b-v0.6.0")

SYSTEM = "You are a senior supply chain expert. Answer correctly and concisely."
user = (
    "I'm an inventory planner at a ~$8M small business: ~11k active SKUs, 4 suppliers, "
    "2 network nodes, ~164-day avg lead time. How should I set safety stock as I move off spreadsheets?"
)
msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": user}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_dict=True, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

**Prompt tip:** Manifest is trained to condition on the scenario — include the asker's role and operating
scale (revenue, SKUs, suppliers, nodes, lead time) in the message for the sharpest, most tailored answers.

## How it was measured

134 held-out expert questions across 8 supply-chain areas. Each question is answered by Manifest and by
the base model (given the same answer format); an independent two-model LLM judge panel then picks the
better answer. Manifest 4B was preferred **97.0%** of the time (95% CI 94.0–99.3%; 129 wins / 3 losses / 2 ties over 134). Benchmark: **[supply-chain-eval](https://huggingface.co/datasets/metafloor-ai/supply-chain-eval)**.

## Training details

| | |
|---|---|
| Method | LoRA (PEFT 0.20.0), rank 16 / alpha 16 / dropout 0.05 |
| Target modules | all attention + MLP projections |
| Trainable params | 21,233,664 (~0.87% of the 2.44B base) |
| Epochs | 3 |
| Training examples | ~32,000 |
| Final loss | 1.09 (from 2.28) |

Training data: supply-chain instruction–response pairs from the seed-driven *operator-as-teacher*
pipeline — a deterministic engine emits a unique seed per example (area, sub-area, persona, question
type, realistic numeric scenario) and a strong teacher model writes the matching answer. **The training data is drawn from MetaFloor's proprietary ~32k-example supply-chain dataset, which is not open-sourced** — only the held-out evaluation benchmark ([supply-chain-eval](https://huggingface.co/datasets/metafloor-ai/supply-chain-eval)) is public.

## Intended use & limitations

- **Intended use:** high-quality decision-support and drafting for supply-chain professionals.
- **Out of scope:** not legally binding, contractual, or safety-critical guidance; no access to your live
  systems or real-time data. Verify outputs before acting on them.
- **Limitations:** English-only; trained on synthetic (model-authored) data; standard LLM risks
  (hallucination, outdated facts) apply.

## License

Manifest models and the [supply-chain-eval](https://huggingface.co/datasets/metafloor-ai/supply-chain-eval) benchmark are released under **CC-BY-NC-4.0** — free for research and non-commercial use, with attribution. **Commercial use requires a license from MetaFloor** — get in touch at [metafloor.ai](https://www.metafloor.ai).

## Built on

Manifest 4B is a LoRA adapter over **Qwen/Qwen3.5-4B** (used under its own license); the base model is required to load the adapter.

## Citation

```bibtex
@misc{metafloor_manifest_4b,
  title  = {Manifest 4B: a supply-chain expert model (MetaFloor Manifest suite)},
  author = {MetaFloor AI},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/metafloor-ai/manifest-orchestrator-4b-v0.6.0}}
}
```

<!-- manifest-suite:order -->