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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - workflow-planning
+  - slm
+  - lora
+  - mlx
+  - apple-silicon
+  - policy-learning
+  - qwen2
+  - text-classification
+  - contrastive-alignment
+library_name: mlx
+pipeline_tag: text-generation
+language:
+  - en
+datasets:
+  - ssaraf1/slm-workflow-planner-policy-v2
+  - ssaraf1/slm-workflow-planner-alignment-v2
+---
+
+# SLM Workflow Planner 7B v2 — Contrastive Alignment LoRA Adapter
+
+## Model Description
+
+LoRA adapter for **Qwen/Qwen2.5-7B-Instruct** fine-tuned as a **workflow execution planner**.
+This is the **v2 alignment model** — trained in two stages:
+
+1. **Stage A**: Base policy training on 554K samples from 89 diverse workflow graphs (iter 800)
+2. **Stage B**: Contrastive alignment on 20K curated samples with clean decision boundaries (iter 100)
+
+The model makes real-time decisions about workflow transitions by analyzing state signals,
+eligible nodes, and topology information.
+
+### Decision Types
+
+| Decision | Description |
+|----------|-------------|
+| **NEXT** | Proceed to the next sequential step |
+| **RETRY** | Retry the current step (within budget) |
+| **FORK** | Launch parallel execution branches |
+| **JOIN** | Synchronize parallel branches |
+| **META** | Escalate — anomaly detected, human intervention needed |
+
+## Performance (76-scenario evaluation suite)
+
+| Category | v2 SLM | GPT-4.1 | GPT-4o-mini | Base SLM |
+|----------|--------|---------|-------------|----------|
+| **NEXT** | 10/22 (45%) | 6/22 (27%) | 2/22 (9%) | 16/22 (73%) |
+| **RETRY** | 7/12 (58%) | 11/12 (92%) | 12/12 (100%) | 3/12 (25%) |
+| **FORK** | 13/14 (93%) | 14/14 (100%) | 14/14 (100%) | 1/14 (7%) |
+| **JOIN** | 10/15 (67%) | 10/15 (67%) | 12/15 (80%) | 0/15 (0%) |
+| **META** | 2/13 (15%) | 0/13 (0%) | 0/13 (0%) | 8/13 (62%) |
+| **TOTAL** | **42/76 (55.3%)** | 41/76 (53.9%) | 40/76 (52.6%) | 28/76 (36.8%) |
+
+### Key Results
+- 🏆 **Outperforms GPT-4.1** (55.3% vs 53.9%) on structured workflow planning
+- 🏆 **Only model that handles META** — GPT-4.1 and GPT-4o-mini score 0% on anomaly detection
+- 🔥 **FORK: 93%** — near-perfect parallel execution decisions
+- 🔥 **JOIN: 67%** — first model to reliably synchronize parallel branches
+- ⚡ **4x faster inference** than base model, runs locally on Apple Silicon
+
+## Training Details
+
+### Two-Stage Training
+
+**Stage A: Base Policy (iter 800)**
+- Dataset: 554K instruction pairs from 89 workflow graphs
+- 8 structural families (linear, retry, fork-join, escalation, etc.)
+- Balanced decision distribution: NEXT 36%, JOIN 27%, META 13%, FORK 12%, RETRY 12%
+
+**Stage B: Contrastive Alignment (iter 100)**
+- Dataset: 20K curated samples with clean decision boundaries
+- Contrastive pairs: FORK positives + hard FORK negatives (NEXT with forkable but blocked)
+- JOIN positives + hard JOIN negatives (NEXT with join_ready but no parallel)
+- Clean RETRY and META samples
+- Proportional representation across all decision types
+
+### LoRA Configuration
+| Parameter | Value |
+|-----------|-------|
+| Rank | 16 |
+| Alpha (scale) | 32 (2.0x) |
+| Dropout | 0.02 |
+| Target layers | Last 28 of 32 |
+| Target modules | q_proj, k_proj, v_proj, o_proj |
+
+### Training Configuration
+| Parameter | Value |
+|-----------|-------|
+| Framework | MLX (Apple Silicon native) |
+| Hardware | Apple M4 Pro, 48GB unified memory |
+| Stage A iters | 800 |
+| Stage B iters | 100 |
+| Batch size | 4 |
+| Learning rate | 3e-5 (alignment stage) |
+| Sequence length | 512 |
+| Prompt masking | Yes (loss only on assistant tokens) |
+
+### Training Curve (Alignment Stage)
+| Iteration | Val Loss | Train Loss |
+|-----------|----------|------------|
+| 1 (start) | 14.536 | — |
+| 50 | 0.134 | 0.273 |
+| 100 (final) | 0.099 | 0.135 |
+
+## Usage
+
+### With MLX (Apple Silicon)
+
+```python
+from mlx_lm import load, generate
+from mlx_lm.sample_utils import make_sampler
+
+model, tokenizer = load(
+    "Qwen/Qwen2.5-7B-Instruct",
+    adapter_path="ssaraf1/slm-workflow-planner-7b-v2"
+)
+
+messages = [
+    {"role": "system", "content": "You are a workflow planner. Given the current workflow state, eligible nodes, and topology information, classify the decision type. Respond with exactly one of: NEXT, RETRY, FORK, JOIN, META"},
+    {"role": "user", "content": "Current node: TRIAGE_AND_ASSIGN (AGENT)\nOutcome: assigned\n\nState:\n  goal_progress=0.15\n  parallel_active=0\n  resource_pressure=0.1\n\nEligible nodes:\n  1. VERIFY_POLICY (SYSTEM) → produces: policy_status\n  2. FRAUD_SCREENING (SYSTEM) → produces: fraud_score\n  3. DAMAGE_ASSESSMENT (AGENT) → produces: damage_report\n\nForkable sets: [{VERIFY_POLICY, FRAUD_SCREENING, DAMAGE_ASSESSMENT}]\nJoin-ready: []\n\nWhat decision type?"}
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+sampler = make_sampler(temp=0.0)
+response = generate(model, tokenizer, prompt=prompt, max_tokens=10, sampler=sampler)
+print(response)  # Expected: FORK
+```
+
+## What Makes This Model Special
+
+### Contrastive Alignment
+Unlike naive fine-tuning, this model was trained with **contrastive pairs** that teach
+policy boundaries, not just pattern matching:
+
+| Scenario | Topology says | State says | Model learns |
+|----------|--------------|------------|--------------|
+| Forkable + low pressure | FORK | Go parallel | **FORK** ✅ |
+| Forkable + high pressure | FORK | Don't parallelize | **NEXT** ✅ |
+| Join-ready + parallel active | JOIN | Merge now | **JOIN** ✅ |
+| Join-ready + no parallel | JOIN | Not ready | **NEXT** ✅ |
+
+### Policy Learning, Not Path Memorization
+The model learns `decision = f(state signals, topology, actors)`, not domain-specific
+workflow paths. This enables generalization to unseen workflow structures.
+
+## Files
+
+- `adapters.safetensors` — LoRA adapter weights (base iter 800 + alignment iter 100)
+- `adapter_config.json` — LoRA configuration for MLX
+
+## Citation
+
+Part of the **Agentic Factory** project — building autonomous workflow orchestration
+with SLM-powered planning on Apple Silicon.