0f6803e551
[fix] replace tool eval() with an AST math evaluator
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execute_tool() relied on signal.SIGALRM, which does not exist on Windows.
The AttributeError was swallowed by a bare except, so every tool call
silently returned None and the model always observed
{"error": "tool not found"} (issue #846 ).
calculate_math also ran eval() on model-generated text. Two of the three
call sites (scripts/eval_toolcall.py, scripts/web_demo.py) passed no
globals at all, leaving full builtins reachable from sampled output.
safe_math_eval() in trainer_utils.py parses the expression with ast and
walks it, allowing only arithmetic operators and a math whitelist, with
caps on expression length and exponent size. It cannot loop forever, so
the signal-based timeout is no longer needed and is removed. Both sqrt(x)
and math.sqrt(x) spellings keep working.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-07 07:53:33 +08:00
6fc918beb6
[update] remove expert args
2026-09-04 22:16:38 +08:00
4bd6062ead
Merge pull request #836 from JustinGuese/moe-cli-flags
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Expose num_experts / num_experts_per_tok / seed on the trainer CLIs
2026-09-04 22:10:18 +08:00
4d7a74acab
Merge pull request #840 from JustinGuese/readme-benchmark-resolution
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docs: state the measurement resolution of the objective benchmark table
2026-09-04 22:10:00 +08:00
77946fda00
[docs] 在客观评测表下补充各数据集的测量分辨率
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lm_eval 对每个指标都会输出 stderr，但表格只保留了点估计。这里按测试集
大小给出各任务的 ± 标准误（obqa ±1.9 到 cmmlu ±0.4），并说明比较两个
模型时差值的标准误约为单项的 √2 倍，方便读者判断表中哪些差距是可以
解读的。中英文 README 同步。
2026-09-03 17:10:52 +07:00
fc585a9bf9
[update] 把随机种子暴露到命令行
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trainer 里的种子此前硬编码为 42（setup_seed(42 + rank) 与每轮的
setup_seed(42 + epoch)），要跑多种子实验只能改源码。新增 --seed，
默认 42，与改动前完全一致。
2026-09-03 15:36:14 +07:00
c3199c7240
[update] 把 num_experts / num_experts_per_tok 暴露到命令行
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此前要改专家数只能去编辑 model_minimind.py。三个入口
（train_pretrain / train_full_sft / eval_llm）的默认值与 MiniMindConfig
一致（4 / 1），默认行为不变；RL 与 agent 相关的 trainer 不动。
2026-09-03 15:13:29 +07:00
7a6fddd63a
[update] readme
2026-08-31 18:08:05 +08:00
c9a8ab4632
[update] dependency version
2026-08-31 17:54:37 +08:00
e84156818a
[update] dependency version
2026-08-29 15:12:28 +08:00
d65ef2c00e
[update] readme content
2026-08-21 16:49:00 +08:00
ed5ea9e02a
[update] readme content
2026-08-21 16:30:30 +08:00
eabad706da
[update] readme content
2026-08-21 16:25:20 +08:00
393e387e9a
[update] supporter charts
2026-08-06 22:07:29 +08:00
89d674b8a5
[refactor] PPO cleanup
2026-07-23 11:11:05 +08:00
2d4c385b0f
[fix] PPO rollout mode
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Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-23 11:05:51 +08:00
e9f46506da
Merge pull request #814 from FloatFrank/fix/ppo-logp-autocast-align-v3
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[fix] PPO: align mb_resp_logp with old_resp_logp under autocast to stabilize training
2026-07-23 10:32:06 +08:00
1191fc8e9f
[fix] PPO: align mb_resp_logp with old_resp_logp under autocast
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Move log_softmax for mb_resp_logp inside autocast_ctx so it is upcast
to fp32, matching the rollout path (compute_per_token_logps runs under
no_grad+autocast). Previously log_softmax ran outside autocast on
fp16/bf16 logits, introducing a ~1e-2 systematic bias between
mb_resp_logp and old_resp_logp, so ratio != 1 and KL != 0 even before
any weight update. Under sparse rule-based rewards (e.g. math PPO)
this bias destabilises training.
Also add:
- --rollout_eval_mode (default 1): switch actor to eval during rollout
to disable dropout and keep old/new logp consistent.
- --debug_log_ratio: optional diagnostic printing |mb-old| on the first
minibatch to verify ratio ~= 1.
2026-07-21 18:51:42 +08:00
512eed0b65
[fix] robustness
2026-06-28 18:03:34 +08:00
3f1a7cc25b
[fix] ddp exit hang
2026-06-01 17:50:39 +08:00
4a68da72d5
[fix] lora dims
2026-05-31 13:38:51 +08:00
4497610ec0
[fix] issue#771
2026-05-19 17:40:03 +08:00
9da8e1ab18
[update] requirements
2026-05-16 18:08:37 +08:00
dddedc6881
[fix] repetition_penalty
2026-05-07 19:08:52 +08:00
802c15b2b4
[feat] reduce RL memory
2026-05-06 15:07:28 +08:00
e73a407f7a
Merge pull request #759 from TKiteRunner/fix/grpo-oom-reorder-rewards
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fix: resolve CUDA OOM in train_grpo.py on GPUs with <=8GB VRAM
2026-05-06 15:00:17 +08:00
10776417aa
fix: resolve CUDA OOM in train_grpo.py on GPUs with <=8GB VRAM
2026-05-06 14:14:15 +08:00
5020dc9dd4
[update] readme
2026-05-06 13:41:46 +08:00
bdee223036
[fix] inference bug
2026-05-03 20:48:48 +08:00
06d882e4ef
[update] readme
2026-05-01 12:06:47 +08:00
da865af63d
[update] readme
2026-04-28 17:22:25 +08:00
773e451b11
[fix] bugs
2026-04-27 19:16:08 +08:00
6361510016
[fix] rollout bugs
2026-04-27 17:54:09 +08:00
d4c6bc5c7e
[update] readme
2026-04-27 10:59:41 +08:00
24896cd2c4
[update] readme
2026-04-24 20:16:06 +08:00
e2fc397176
[update] readme
2026-04-24 20:14:10 +08:00
693fb1ccf1
[update] readme
2026-04-21 14:34:46 +08:00
5416a44471
[fix] bugs
2026-04-21 13:03:34 +08:00
1718e9a44d
[fix] transformers-5.x
2026-04-19 23:48:54 +08:00
5704766352
[update] tie embedding
2026-04-19 21:57:28 +08:00
1ea113ea2c
[update] readme
2026-04-19 14:51:47 +08:00
487f78754d
[update] readme
2026-04-10 10:55:24 +08:00
e796b8028a
[fix] lora compile
2026-04-10 00:01:21 +08:00
b2488e6440
[update] readme
2026-04-09 19:00:41 +08:00
939dc8ff42
[update] readme
2026-04-09 18:54:17 +08:00
cadacabecb
[update] bench
2026-04-09 17:12:48 +08:00
5351424bf0
[fix] lora moe
2026-04-09 16:36:48 +08:00
aa3e6affa1
[update] add a comment
2026-04-09 15:09:58 +08:00
25a7edcd6f
[update] image
2026-04-04 14:54:13 +08:00
cacf1d4cd0
[update] readme
2026-04-04 11:25:21 +08:00
tkgcc
jingyaogong
Justin Guese
FloatFrank
TKiteRunner
jingyaogong