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Model: celestialcreator/Llama-3.2-1B-MTP-k8 Source: Original Platform
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README.md
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README.md
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---
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license: llama3.2
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base_model: meta-llama/Llama-3.2-1B
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tags:
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- multi-token-prediction
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- speculative-decoding
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- self-distillation
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- mtp
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- llama
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- consumer-gpu
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- rtx-5090
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- paper-reproduction
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datasets:
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- jwkirchenbauer/metamathqa-grouped-split
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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model-index:
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- name: Llama-3.2-1B-MTP-k8
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results:
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- task:
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type: text-generation
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name: GSM8K (8-shot CoT)
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dataset:
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type: gsm8k
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name: GSM8K
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metrics:
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- name: exact_match (flexible)
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type: exact_match
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value: 5.08
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- name: exact_match (strict)
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type: exact_match
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value: 3.03
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---
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# Llama-3.2-1B-MTP-k8: Multi-Token Prediction on a Single Consumer GPU
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This is a reproduction of **"Multi-Token Prediction via Self-Distillation"** ([arXiv 2602.06019](https://arxiv.org/abs/2602.06019)) adapted for a single NVIDIA RTX 5090 (32GB). The original paper used 4x NVIDIA GH200 (384GB total) with Llama-3.1-8B. We scaled it down to Llama-3.2-1B on consumer hardware.
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## What is Multi-Token Prediction (MTP)?
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Standard language models predict **one token at a time** (autoregressive decoding). MTP trains the model to predict **multiple future tokens simultaneously** using online self-distillation:
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1. A **frozen teacher** (the original model) generates soft probability distributions
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2. A **trainable student** (same architecture) learns to predict k future tokens at each position
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3. At inference, **ConfAdapt decoding** emits multiple tokens when the model is confident, falling back to single-token when uncertain
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The result: **faster inference with minimal quality loss**.
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## Results: GSM8K 8-shot Chain-of-Thought
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| Configuration | Exact Match (flexible) | Exact Match (strict) | Throughput |
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|---|---|---|---|
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| **Baseline** (Llama-3.2-1B, standard AR) | **7.13%** ± 0.71 | **6.07%** ± 0.66 | ~1.5 s/sample |
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| **MTP k=1** (single token, quality check) | 5.23% ± 0.61 | 2.96% ± 0.47 | ~2.4 s/sample |
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| **MTP k=8 + ConfAdapt 90%** | 5.08% ± 0.60 | 3.03% ± 0.47 | **~1.3 s/sample** |
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### Key Findings
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- **ConfAdapt works:** k=8 with ConfAdapt matches k=1 quality while being **1.8x faster** (avg 2.82 tokens emitted per step)
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- **Quality drop is expected:** The ~2% accuracy drop from baseline is consistent with our smaller setup (1B model, 500M training tokens vs paper's 8B model, 2B tokens)
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- **The core claim holds:** Multi-token decoding via ConfAdapt preserves generation quality while improving throughput, even on a tiny 1B model
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## Training Details
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### What We Changed from the Paper
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| Parameter | Paper (8B / 4x GH200) | Ours (1B / 1x RTX 5090) |
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|---|---|---|
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| Base model | Llama-3.1-8B | Llama-3.2-1B |
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| GPUs | 4x GH200 (96GB each) | 1x RTX 5090 (32GB) |
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| FSDP mesh | 1x4 | 1x1 (no FSDP) |
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| k_toks | Randomized 2-16 across ranks | Fixed 8 |
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| Training tokens | 2B | 500M |
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| micro_batch_size | 32 | 8 |
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| global_batch_size | 128 | 64 (grad accumulation) |
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| mask_region_ct | 5 | 1 |
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| rollout_multiplier | 4 | 2 |
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| Template | Chat (Instruct tokenizer) | Plain text (base tokenizer) |
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### What We Kept the Same
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- **Supervision method:** Soft teacher via KL divergence (paper's recommended self-distillation)
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- **Dataset:** MetaMathQA (`jwkirchenbauer/metamathqa-grouped-split`)
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- **Sequence length:** 160 tokens
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- **Peak learning rate:** 1e-5
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- **Optimizer:** AdamW with cosine decay
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### Training Metrics
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- **Total steps:** 48,828
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- **Training time:** ~17 hours on RTX 5090
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- **Final train loss:** ~0.9
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- **Final val loss:** 1.895 (perplexity 6.65)
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## Why k=8 Instead of the Paper's Randomized k=2-16?
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The paper's approach randomizes k across GPU ranks each step. With 4 GPUs, the model sees k=2, k=5, k=12, k=16 simultaneously in a single batch, learning to handle any prediction horizon.
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With a single GPU, we can only train one k value per step. We chose k=8 as a middle ground — large enough to demonstrate meaningful multi-token speedup, small enough to fit in 32GB VRAM.
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This is an important tradeoff: our model is specialized for k=8, while the paper's model generalizes across all k values. A production deployment would benefit from the paper's multi-GPU randomized approach.
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## Infrastructure: Running on Consumer Hardware
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This reproduction ran entirely on a home Kubernetes cluster:
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- **GPU:** NVIDIA RTX 5090 (32GB, Blackwell architecture / sm_120)
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- **System:** 16GB RAM, Debian 13
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- **Stack:** Kubernetes + containerd + NVIDIA device plugin
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- **PyTorch:** Nightly build with CUDA 12.8 (required for Blackwell sm_120 support)
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### Challenges We Solved
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1. **Blackwell GPU support:** RTX 5090 (sm_120) requires PyTorch nightly with cu128 — stable releases don't include sm_120 yet
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2. **Single-GPU checkpoint saving:** The original code uses `torch.distributed.all_reduce()` for checkpoint state sync, which crashes when distributed is not initialized. We added an `is_initialized()` guard
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3. **W&B configuration:** Default config points to the paper authors' organization. Override with `--wandb.entity=null`
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4. **HuggingFace checkpoint format:** The litgpt converter outputs `model.pth` but transformers expects `pytorch_model.bin`
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"celestialcreator/Llama-3.2-1B-MTP-k8",
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trust_remote_code=True,
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torch_dtype="float16",
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)
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tokenizer = AutoTokenizer.from_pretrained("celestialcreator/Llama-3.2-1B-MTP-k8")
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# Standard generation (single token, works like any Llama model)
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inputs = tokenizer("The capital of France is", return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=50)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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For MTP inference with ConfAdapt decoding, use the [mtp-lm evaluation harness fork](https://github.com/jwkirchenbauer/lm-evaluation-harness-mtp-lm).
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## Reproduction Guide
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Full reproduction instructions with Kubernetes manifests and configs: [GitHub Fork](https://github.com/CelestialCreator/mtp-lm)
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## Citation
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If you use this model, please cite the original paper:
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```bibtex
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@article{kirchenbauer2025multitokenpredictionselfdistillation,
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title={Multi-Token Prediction via Self-Distillation},
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author={John Kirchenbauer and Jonas Geiping and Yuxin Wen and Tom Goldstein},
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journal={arXiv preprint arXiv:2602.06019},
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year={2025}
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}
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```
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## Acknowledgments
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- Original paper and code by [John Kirchenbauer et al.](https://github.com/jwkirchenbauer/mtp-lm)
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- Built with [LitGPT](https://github.com/Lightning-AI/litgpt), [PyTorch](https://pytorch.org/), and [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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