Emerging ML Techniques Workspace

Overview

A comprehensive skill for exploring and implementing cutting-edge machine learning techniques — covering mixture of experts (MoE), speculative decoding, knowledge distillation, model merging, quantization-aware training, constitutional AI, and other emerging approaches that push the boundaries of model performance, efficiency, and alignment.

When to Use

• Exploring state-of-the-art ML techniques
• Implementing novel architectures (MoE, SSMs, etc.)
• Optimizing inference with speculative decoding
• Merging models with TIES, DARE, or SLERP
• Applying constitutional AI principles
• Implementing efficient attention mechanisms
• Experimenting with post-training optimization

Quick Start

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# Model merging
pip install mergekit
mergekit-yaml merge_config.yaml ./merged_model

# Speculative decoding
pip install transformers accelerate
python speculative_decode.py --draft-model small --target-model large

# Quantization-aware training
pip install bitsandbytes auto-gptq

Emerging Techniques Overview

1. Mixture of Experts (MoE)

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import torch
import torch.nn as nn

class MoELayer(nn.Module):
    def __init__(self, hidden_size, num_experts=8, top_k=2):
        super().__init__()
        self.num_experts = num_experts
        self.top_k = top_k
        self.gate = nn.Linear(hidden_size, num_experts)
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(hidden_size, hidden_size * 4),
                nn.GELU(),
                nn.Linear(hidden_size * 4, hidden_size),
            )
            for _ in range(num_experts)
        ])
    
    def forward(self, x):
        # Router: select top-k experts per token
        gate_logits = self.gate(x)  # (batch, seq, num_experts)
        weights, indices = torch.topk(gate_logits, self.top_k, dim=-1)
        weights = torch.softmax(weights, dim=-1)
        
        # Compute weighted expert outputs
        output = torch.zeros_like(x)
        for i, expert in enumerate(self.experts):
            mask = (indices == i).any(dim=-1)
            if mask.any():
                expert_out = expert(x[mask])
                weight = weights[indices == i].unsqueeze(-1)
                output[mask] += expert_out * weight
        
        return output

2. Speculative Decoding

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def speculative_decode(target_model, draft_model, input_ids, gamma=5):
    """Generate tokens faster using a small draft model"""
    generated = input_ids.clone()
    
    while len(generated[0]) < max_length:
        # Draft model generates gamma candidate tokens
        draft_tokens = []
        draft_probs = []
        current = generated
        
        for _ in range(gamma):
            with torch.no_grad():
                logits = draft_model(current).logits[:, -1]
                probs = torch.softmax(logits, dim=-1)
                token = torch.multinomial(probs, 1)
                draft_tokens.append(token)
                draft_probs.append(probs)
                current = torch.cat([current, token], dim=-1)
        
        # Target model verifies all at once (single forward pass)
        with torch.no_grad():
            target_logits = target_model(current).logits
        
        # Accept/reject draft tokens
        accepted = 0
        for i in range(gamma):
            target_probs = torch.softmax(target_logits[:, -(gamma - i) - 1], dim=-1)
            draft_prob = draft_probs[i].gather(-1, draft_tokens[i])
            target_prob = target_probs.gather(-1, draft_tokens[i])
            
            # Accept if target agrees
            if torch.rand(1) < (target_prob / draft_prob).clamp(max=1.0):
                generated = torch.cat([generated, draft_tokens[i]], dim=-1)
                accepted += 1
            else:
                # Sample from adjusted distribution
                adjusted = torch.clamp(target_probs - draft_probs[i], min=0)
                adjusted = adjusted / adjusted.sum()
                token = torch.multinomial(adjusted, 1)
                generated = torch.cat([generated, token], dim=-1)
                break
    
    return generated

3. Model Merging

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# mergekit config — SLERP merge
models:
  - model: model_a
    parameters:
      weight: 0.6
  - model: model_b
    parameters:
      weight: 0.4
merge_method: slerp
base_model: model_a
parameters:
  t: 0.5
dtype: bfloat16

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# TIES merge (resolves parameter conflicts)
mergekit-yaml ties_config.yaml ./output --cuda

# DARE merge (drop and rescale)
mergekit-yaml dare_config.yaml ./output --cuda

Technique Comparison

Technique	Benefit	Complexity	Maturity
MoE	Scale params without scale compute	High	Production
Speculative Decoding	2-3x faster inference	Medium	Production
Knowledge Distillation	Compress models	Medium	Mature
Model Merging	Combine capabilities	Low	Experimental
QAT	Quantize with minimal quality loss	Medium	Mature
Constitutional AI	Align model behavior	Medium	Production
State Space Models	Linear-time sequences	High	Emerging
Ring Attention	Ultra-long context	High	Research

Best Practices

1. Start with the simplest technique — Model merging and distillation before MoE or SSMs
2. Benchmark rigorously — Use standardized evals (MMLU, HumanEval, etc.) to measure technique impact
3. Combine techniques — Distill + merge + quantize for maximum efficiency
4. Monitor expert utilization in MoE — Imbalanced routing wastes capacity
5. Tune draft model carefully for speculative decoding — Draft must be fast AND accurate
6. Version everything — Model weights, configs, and eval results for reproducibility
7. Test on diverse tasks — Novel techniques may help some tasks while hurting others
8. Read the papers — Implementation details matter; subtle differences affect results significantly
9. Start small — Test techniques on small models before scaling to production size
10. Stay current — This field moves fast; check arXiv and HuggingFace weekly

Troubleshooting

MoE expert collapse (all tokens route to same expert)

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# Add load balancing loss
from torch.nn import functional as F

def load_balance_loss(gate_logits, num_experts):
    # Encourage uniform expert utilization
    probs = F.softmax(gate_logits, dim=-1)
    avg_probs = probs.mean(dim=0)  # Average across tokens
    uniform = torch.ones_like(avg_probs) / num_experts
    return F.kl_div(avg_probs.log(), uniform, reduction='batchmean')

Speculative decoding acceptance rate too low

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# Use a better draft model or reduce gamma
# Monitor acceptance rate
acceptance_rate = accepted / gamma
if acceptance_rate < 0.5:
    gamma = max(2, gamma - 1)  # Generate fewer draft tokens

Model merge produces garbage

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# Use SLERP with lower interpolation
# Or use TIES/DARE to resolve parameter conflicts
# Ensure models share the same tokenizer and architecture