初始化项目，由ModelHub XC社区提供模型

Model: zcyzcyzcy/qwen3-1.7b-jf-v2math811-ar10
Source: Original Platform

README.md

@@ -0,0 +1,99 @@
+---
+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.