sameer-saraf-quant-ai/slm-workflow-planner-7b-v3

SLM Workflow Planner 7B v3 — Fork-Suppression Alignment (Best Overall)

Model Description

LoRA adapter for Qwen/Qwen2.5-7B-Instruct fine-tuned as a workflow execution planner. This is the v3 model — the best-performing checkpoint across all training phases, trained in three stages:

1. 1.Stage A: Base policy training on 554K samples from 89 diverse workflow graphs (iter 800)
2. 2.Stage B: Contrastive alignment on 20K curated samples with clean decision boundaries (iter 100)
3. 3.Stage C: Fork-suppression alignment on 4.6K targeted samples to fix FORK over-triggering (iter 200)

The model makes real-time decisions about workflow transitions by analyzing state signals, eligible nodes, and topology information.

Decision Types

Performance (76-scenario evaluation suite)

Key Results

• —🏆 Best overall accuracy: 59.2% — outperforms all previous versions and GPT-4.1
• —🔥 RETRY: 100% — perfect retry handling (was 58% in v2)
• —🔥 FORK: 86% — strong parallel execution decisions with correct suppression
• —🔥 NEXT: 68% — massive improvement over v2 (36%) without collapse to NEXT
• —⚡ Balanced policy — the only checkpoint that achieves strong NEXT + RETRY + FORK simultaneously
• —⚡ 4x faster inference than base model, runs locally on Apple Silicon

Architecture Evolution

v3 fixes v2's FORK over-triggering problem. v2 had learned "forkable → FORK" blindly. v3 correctly learns "forkable AND conditions favorable → FORK, otherwise NEXT".

Training Details

Three-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 negatives, JOIN positives + hard negatives
• —Proportional representation across all decision types

Stage C: Fork Suppression (iter 200)

• —Dataset: 4,600 targeted samples
• —Focus: "forkable but blocked → NEXT" hard negatives
• —Teaches: resource pressure, parallel depth, uncertainty block FORK
• —Stabilizers: RETRY and NEXT anchors to prevent forgetting

LoRA Configuration

Training Configuration

Usage

With MLX (Apple Silicon)

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-v3"
)

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: VERIFY_POLICY (SYSTEM)\nOutcome: success\n\nState:\n  goal_progress=0.35\n  parallel_active=0\n  resource_pressure=0.1\n  retry_count=0\n\nEligible nodes:\n  1. FRAUD_SCREENING (SYSTEM) → produces: fraud_score\n  2. DAMAGE_ASSESSMENT (AGENT) → produces: damage_report\n\nForkable sets: [{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 (low pressure, independent actors)

What Makes v3 Special

Fork Suppression — Correct Policy Boundaries

v2 over-triggered FORK whenever forkable_sets was present. v3 learned the correct policy:

Remaining Challenges (v4 targets)

• —JOIN: 40% — model struggles with join synchronization
• —META: 0% — anomaly detection not yet learned
• —These require a unified alignment approach (not sequential patching)

Files

• —adapters.safetensors — LoRA adapter weights (Stage A + B + C)
• —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.