ssaraf1/slm-workflow-planner-7b-v1

SLM Workflow Planner 7B v1 — LoRA Adapter

Model Description

LoRA adapter for Qwen/Qwen2.5-7B-Instruct fine-tuned as a workflow execution planner. The model makes real-time decisions about workflow transitions by analyzing state signals, eligible nodes, and topology information.

Decision Types

Training Details

Base Model

• —Model: Qwen/Qwen2.5-7B-Instruct
• —Parameters: 7.6B (40.4M trainable via LoRA = 0.53%)

LoRA Configuration

Training Configuration

Training Data

• —Dataset: ssaraf1/slm-workflow-planner-policy-v2
• —Note: This v1 model was trained on the original (pre-policy-correction) data from 89 synthetic workflow graphs. Decision labels were topology-based, not policy-conditioned. A v2 model trained on policy-corrected data is forthcoming.

Training Curve

Performance (v1 — Pre-Policy-Correction)

Evaluated on 20 representative scenarios across Workshop and Insurance Claim domains:

Key Insight

This model learned protocol (single-token planner output) and NEXT progression perfectly, but suffers from NEXT-dominance due to imbalanced pre-correction training data. The v2 model addresses this with policy-corrected labels and counterfactual negatives.

What This Model Does Well

• —✅ Produces valid planner vocabulary (NEXT/RETRY/FORK/JOIN/META)
• —✅ Single-token structured output
• —✅ 4× faster inference than base model
• —✅ Perfect NEXT decision accuracy
• —✅ Recognizes policy boundaries (forkable set + high resource → NEXT)

Known Limitations

• —❌ NEXT-dominant collapse on non-NEXT decisions
• —❌ Trained on topology-only labels (not state-conditioned)
• —❌ Single-workflow overfitting (89 synthetic graphs)

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-v1"
)

messages = [
    {"role": "system", "content": "You are a workflow planner. Given the current workflow state, eligible nodes, and topology information, classify the decision type."},
    {"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

Architecture

This is a two-stage planner:

1. 1.Stage 1: Classify decision type → NEXT / RETRY / FORK / JOIN / META
2. 2.Stage 2: Select node(s) from eligible candidates based on decision type

The adapter handles both stages via the same LoRA weights.

Files

• —adapters.safetensors — LoRA adapter weights (checkpoint iter 2600)
• —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.