---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
tags:
  - jacobi-forcing
  - speculative-decoding
  - qwen3
  - text-generation
language:
  - en
pipeline_tag: text-generation
---

# Qwen3-1.7B Jacobi Forcing (v2math811, AR×10)

Jacobi-Forcing fine-tune of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) trained on a mixed code + math trajectory dataset (`v2math811`). Produces output identical in quality to the base AR model while supporting **Jacobi parallel decoding for ~1.5–1.7× wall-clock speedup**.

## Highlights

- **Lossless quality**: HumanEval pass@1 / GSM8K accuracy match base AR generation (within noise).
- **Speedup**: 1.65× on HumanEval, 1.53× on GSM8K (vs greedy AR, same model).
- **Drop-in compatible** with HuggingFace `AutoModelForCausalLM` for AR generation. Jacobi inference requires the [JacobiForcing repo](https://github.com/) (custom forward kernel).

## Training recipe

Continued from base Qwen3-1.7B with the consistency + AR loss from the [JacobiForcing](https://arxiv.org/abs/2403.00835) paper:

| Setting | Value |
| --- | --- |
| Base | `Qwen/Qwen3-1.7B` |
| Dataset | code (OpenCodeInstruct buckets 8-11) + math (OpenThought2 buckets 8-11), 26 510 trajectory samples after traj_count ≤ 3 filter |
| Strategy | progressive noise window, N=32, window=16 |
| Epochs | 1 |
| Optimizer | AdamW |
| LR | 5e-6 (cosine, warmup 0.03) |
| Batch | per-device 1 × grad-accum 4 = 4 |
| Precision | bf16 |
| `AR_LOSS_WEIGHT` | **10** (paper default; tested 20 — slightly worse Jacobi acceptance) |
| GPU | 1× A100-80GB, ~4h47m |

## Benchmarks (1× A100, greedy)

| Bench | AR pass@1 / acc | Jacobi pass@1 / acc | AR tok/s | Jacobi tok/s | Speedup |
| --- | ---: | ---: | ---: | ---: | ---: |
| HumanEval (n=164) | 60.4 % | **61.0 %** | 37.2 | 61.3 | **1.65×** |
| GSM8K (n=653 subset) | 72.4 % | **74.3 %** | 38.0 | 58.3 | **1.53×** |

Jacobi internals (HumanEval): tok/iter = 1.74, average accept-window 87 % of N=32.

## Usage — standard AR

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

ckpt = "zcyzcyzcy/qwen3-1.7b-jf-v2math811-ar10"
tok = AutoTokenizer.from_pretrained(ckpt)
model = AutoModelForCausalLM.from_pretrained(
    ckpt, torch_dtype=torch.bfloat16, device_map="cuda"
)

msgs = [{"role": "user", "content": "Write a Python is_prime(n)."}]
inp = tok.apply_chat_template(
    msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
ids = tok(inp, return_tensors="pt").to("cuda")
out = model.generate(**ids, max_new_tokens=200, do_sample=False)
print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Usage — Jacobi parallel decoding

Jacobi inference uses a custom `jacobi_forward_greedy` registered on `Qwen3ForCausalLM`. See the [JacobiForcing repo](https://github.com/) for the full inference script, or use the snippet:

```python
from transformers import Qwen3ForCausalLM
from generate_trajectory.generation.qwen3_modeling_jacobi_forcing_greedy import (
    jacobi_forward_greedy,
)
Qwen3ForCausalLM.jacobi_forward_greedy = jacobi_forward_greedy
# ... call model.jacobi_forward_greedy(...) for prefill + generation phases.
```

The model checkpoint itself is a standard Qwen3 — no architecture changes — so any speculative-decoding framework that accepts a Qwen3 base model can drive it.

## Citation

```bibtex
@article{kou2024cllm,
  title={CLLMs: Consistency Large Language Models},
  author={Kou, Siqi and Hu, Lanxiang and He, Zhezhi and Deng, Zhijie and Zhang, Hao},
  journal={arXiv preprint arXiv:2403.00835},
  year={2024}
}
```

## License

Apache 2.0, inherited from the base Qwen3-1.7B model.
