7. Post-Training Improvement Ladder

Each stage improves the model and exercises a different entrenar / apr capability. Every stage produces a benchmarked checkpoint.

7.1 Stage 1: Pre-Train Base Model

apr train plan configs/train/pretrain-350m.yaml          # Validate + VRAM estimate
apr train apply configs/train/pretrain-350m.yaml --seed 42

Produces: albor-base-350m — raw pre-trained model Exercises: entrenar, trueno (CUDA), alimentar (data streaming) Expected: OPT-350M class on general benchmarks (~48% avg). On HumanEval, target >8% (above random, below CodeGen-350M’s 12.8% due to less training data)

7.2 Stage 2: Knowledge Distillation from Qwen3-Coder-Next

# Plan: check teacher fits in RAM, estimate logit disk usage
apr distill plan configs/train/distill.yaml

# Apply phase 1: Pre-compute teacher logits on intel (300GB RAM, CPU inference)
apr distill apply configs/train/distill.yaml --stage precompute

# Apply phase 2: Distill into student on lambda (4090)
apr distill apply configs/train/distill.yaml --stage train

Produces: albor-distill-350m — distilled model with teacher knowledge Exercises: realizar (teacher inference), apr distill, alimentar (logit storage) Expected: Moderate improvement — absorbs coding patterns from 80B teacher. Estimated +2-7 points on HumanEval via logit-level KD. Note: MoE→dense distillation is uncharted at this scale; the architecture mismatch (DeltaNet+MoE teacher → LLaMA-style dense student) may limit transfer compared to dense→dense distillation (e.g., GPT-3.5→phi-1).

7.3 Stage 3: Instruction Fine-Tuning (LoRA/QLoRA)

apr finetune plan configs/train/finetune-lora.yaml        # Validate LoRA config + VRAM
apr finetune apply configs/train/finetune-lora.yaml

Produces: albor-instruct-350m — instruction-following model Exercises: apr finetune, entrenar LoRA, alimentar (JSONL instruction data) Expected: Better IFEval scores, improved structured output, chat capability.

7.4 Stage 4: Model Merging

apr merge plan \
  --models albor-distill-350m,albor-instruct-350m \
  --method slerp --weight 0.6 \
  --output ./checkpoints/albor-merged/
# Plan checks: architectures compatible, method valid, output size estimate

apr merge apply \
  --models albor-distill-350m,albor-instruct-350m \
  --method slerp --weight 0.6 \
  --output ./checkpoints/albor-merged/

Produces: albor-merged-350m — best-of-all-worlds model Exercises: apr merge (SLERP, TIES, DARE algorithms) Expected: Cherry-picks strengths from each variant. Potentially better than any single model on diverse benchmarks.

7.5 Stage 5: Pruning

apr prune plan \
  --model ./checkpoints/albor-merged-350m/ \
  --method wanda --sparsity 0.5 \
  --output ./checkpoints/albor-pruned/
# Plan checks: model exists, sparsity in [0,1], output size estimate

apr prune apply \
  --model ./checkpoints/albor-merged-350m/ \
  --method wanda --sparsity 0.5 \
  --output ./checkpoints/albor-pruned/

Produces: albor-pruned-175m — half the parameters, similar performance Exercises: apr prune (WANDA, SparseGPT, magnitude, depth pruning) Expected: ~2-5% benchmark degradation at 50% sparsity. WANDA is well-studied at larger scales (7B+) but less validated at 350M where there is less redundancy. Depth pruning to ~18 layers yields ~260M params.

7.6 Stage 6: Quantization

apr quantize plan \
  --model ./checkpoints/albor-merged-350m/ \
  --method q4_k \
  --output ./checkpoints/albor-q4/
# Plan checks: model exists, format valid, output size estimate (~90MB)

apr quantize apply \
  --model ./checkpoints/albor-merged-350m/ \
  --method q4_k \
  --output ./checkpoints/albor-q4/

# Export for broad compatibility
apr export plan --model ./checkpoints/albor-q4/ --format gguf
apr export apply \
  --model ./checkpoints/albor-q4/ \
  --format gguf \
  --output ./release/albor-350m-q4_k.gguf

Produces: albor-q4-350m — 4-bit quantized, ~90MB on disk Exercises: apr quantize, apr export (GGUF, SafeTensors) Expected: <1% benchmark loss from Q4_K quantization. Model runs on any device — phones, Raspberry Pi, browsers (WASM via trueno).

7.7 Benchmark Trajectory

Every stage is benchmarked. The trajectory itself is a key result. Code completion metrics (HumanEval, FIM) are primary; general benchmarks are secondary.

Numbers are estimates. The distillation gain (+2-7 points over base) assumes 500M-2B tokens of teacher logits. This is conservative — published distillation results show larger gains with dense teachers (phi-1 used GPT-3.5, a dense model). Our MoE→dense distillation path is uncharted at 350M scale. The FIM column is removed because there is no standardized FIM benchmark — we will define our own eval and report absolute numbers, not targets. CPU tok/s measured on Xeon at Q4.