alea-institute/kl3m-006-170m-checkpoint-37000

KL3M 170M, 6th Gen Model, 37K Checkpoint

A 170M parameter language model trained on legal agreements using the Muon optimizer with spectral clamping.

Model Details

• Architecture: Llama-based with Grouped Query Attention (GQA)
• Parameters: 181.7M (170M non-embedding)
• Training Steps: 37,000
• Tokens Processed: 14.55 billion
• Sequence Length: 4,096 tokens
• Precision: BF16
• Optimizer: Muon with spectral regularization (max condition: 2000)

Model Architecture

• Hidden Size: 576
• Layers: 30
• Attention Heads: 9 (3 KV heads with GQA)
• Intermediate Size: 1536
• Vocabulary: 131,072 tokens
• RoPE Theta: 100,000

Training Configuration

Dataset

• Source: alea-institute/kl3m-data-sample-002-shuffled
• Type: Legal agreements (EDGAR filings, contracts)
• Format: Streaming, shuffled

Optimizer (Muon)

• Muon Learning Rate: 2.19e-4 (depth-scaled from 3e-4)
• Auxiliary Learning Rate: 3e-4
• Muon Weight Decay: 1e-4
• Auxiliary Weight Decay: 1e-3
• Muon Momentum: 0.95
• Batch Size: 6 per device
• Gradient Accumulation: 16 steps (effective batch: 96)
• Warmup Steps: 1,000
• LR Scheduler: Cosine with min ratio 0.1

Regularization

• Spectral Clamping:
  • Enabled on q_proj, o_proj, and lm_head
  • Max condition number: 2000
  • Sigma floor: 1e-4
  • Applied every 10 steps (every 960 samples)
• Adaptive Gradient Clipping: Enabled (β=0.9, coeff=2.0)
• Label Smoothing: 0.01
• Entropy Regularization:
  • Entropy bonus weight: 0.005
  • Entropy target: 6.5 bits (weight: 0.005)
  • Activation norm weight: 0.001
  • Loss chunk size: 1024 tokens

Training Infrastructure

• Mixed Precision: BF16
• Gradient Checkpointing: Enabled (non-reentrant)
• Flash Attention: Auto-enabled
• TF32 Mode: Auto

Spectral Health (Step 37K)

Analysis of weight matrix conditioning shows excellent manifold quality:

Condition Numbers

• Attention Layers:
  • Median: 349.79 ✓ EXCELLENT
  • Mean: 784.46
  • P95: 2000.15 (at spectral clamp ceiling)
  • Max: 2000.26
• MLP Layers:
  • Median: 4.70 ✓ EXCELLENT
  • Mean: 4.86
  • Max: 8.40
• LM Head: 261.88 ✓ GOOD

Singular Values

• Smallest σ_min: 5.42e-4 (well above σ_floor of 1e-4)
• Top 5 smallest:
  • layers.0.self_attn.o_proj: 5.42e-4
  • layers.5.self_attn.o_proj: 5.44e-4
  • layers.9.self_attn.o_proj: 6.07e-4
  • layers.22.self_attn.o_proj: 6.09e-4
  • layers.4.self_attn.o_proj: 6.15e-4

Key Finding: Many attention projection layers (q_proj, o_proj) are actively hitting the spectral clamp ceiling of 2000, indicating the regularization is working as intended to prevent ill-conditioning.

Training Dynamics (Steps 33K-37K)

• Loss (100-step avg): 2.081 → 2.146 (+0.066)
  • Note: Slight increase suggests model encountered harder examples or dataset shift
• Gradient Norm: Median 1.90, P95 4.29
• Gradient Clipping Rate: 3.6% (well-controlled)
• Learning Rate: Minimal decay (0.000219 → 0.000218)

Generation Quality

Generates coherent, fluent legal text with no repetition issues. Suitable for legal/contractual content generation and analysis.

Usage

from transformers import pipeline

# Create text generation pipeline
generator = pipeline(
    "text-generation",
    model="alea-institute/kl3m-006-170m-checkpoint-37000",
    torch_dtype="auto",
    device_map="auto"
)

# Generate text
outputs = generator(
    "This Agreement is entered into as of",
    max_new_tokens=100,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(outputs[0]['generated_text'])

Model Card Authors

Alea Institute

Citation

For technical details, see the paper: https://arxiv.org/abs/2504.07854

@misc{kl3m2025,
  title={KL3M: Knowledge-Guided Language Model Training},
  author={Alea Institute},
  year={2025},
  url={https://arxiv.org/abs/2504.07854},
  note={Trained with Muon optimizer and spectral clamping}
}

License

Apache 2.0