Stage 1 on HF Jobs: Unsloth job script, PEFT to MLX converter, base valid loss 0.849, Qwen base model docs

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

train/README.md

@@ -10,17 +10,16 @@ Task: `docs/stage1-training-task.md`. State of the work: this file and `train/ST
 ## Base model
 
 - Base model: **`Qwen/Qwen3.8-27B`** (architecture `qwen3_5`, 27.8B, dense; Apache 2.0). Kral decision 2026-10-04
-  (revised the same day: Qwen was dropped for a few hours, Devstral Small 2 was a candidate; Kral chose Qwen).
+  (Qwen was dropped for a few hours, Devstral Small 2 was tested; Kral chose Qwen again).
 - Weights: **`mlx-community/Qwen3.8-27B-4bit`** (MLX affine, 4 bit, group size 64, 16.1 GB), local path
   `~/models/Qwen3.8-27B-4bit`. Why not the Ollama weights: they are NVFP4 with a global scale per layer; `mlx_lm`
   cannot load them without a re-quantization.
-- Devstral Small 2 (`mlx-community/Devstral-Small-2-24B-Instruct-2512-4bit`, `~/models/Devstral-Small-2-24B-4bit`)
-  was tested only for the baseline: `runs/stage1/baseline_devstral.md` (mean 6.8, 1 of 11 tasks above 0). Not used further.
+- Devstral Small 2 was tested for the baseline only and dropped: mean 6.8 vs Qwen 15.8, 1 vs 3 of 11 tasks above 0,
+  loops 6 vs 7. Results: `runs/archive/devstral/`. Weights deleted. No more base model tests.
 - Known Qwen weaknesses (the training target): no repair after activation errors, loops (same source pushed again),
   empty responses at the thinking limit when thinking is on. Thinking stays off in stage 1.
 - Stage 1 reference baseline: **Qwen, mean 15.8, 3 of 11 tasks above 0** (T01 48.3, G0157 51.0, G0185 75.0), same settings as Devstral, run on the MacBook (`runs/stage1/baseline_qwen.json`, run base 22000; copy `baseline.json`). Comparison: `docs/stage1-baseline.md`.
-  `train/serve.sh` serves Devstral at the moment; for Qwen use `train/serve_qwen.sh` (in the MacBook package) or
-  restore the Qwen line (`--model ~/models/Qwen3.8-27B-4bit`, `--chat-template-args` as in git history before 0af2d64).
+  `train/serve.sh` serves Qwen (commit a2ba9e7).
 
 ## Serving (`train/serve.sh`)
 
@@ -29,17 +28,14 @@ training the same script with `--adapter-path`.
 
 | Setting | Value |
 |---|---|
-| Thinking | none (Devstral has no thinking mode); no `chat_template_kwargs` are sent |
+| Thinking | server default on (`--chat-template-args`); `train/baseline.py` sends `enable_thinking=false` per request |
 | temperature | 0.2 (sent by the harness llm agent; the model card suggests 0.15) |
 | top_p / top_k / min_p | 0.95 / 20 / 0 (server flags) |
 | presence / repeat penalty | not set (neutral) |
 | Output limit | server `--max-tokens 32768`; each request sends 16384 |
 | Prompt cache | `--prompt-cache-size 4 --prompt-cache-bytes 6000000000` |
 
-Tool calls (2026-10-04): the chat template uses the Mistral format (`[AVAILABLE_TOOLS]`, `[TOOL_CALLS]name[ARGS]{json}`);
-`mlx_lm` returns OpenAI `tool_calls` with JSON arguments. The smoke test T01 and the baseline had no parse errors.
-Known behaviour: some turns have prose and no tool call; the harness takes such a turn as the final report
-(G0128, G0174 ended with `report`).
+Tool calls: `mlx_lm` returns OpenAI `tool_calls` with JSON arguments (tool-call test passed).
 
 ## Eval subset
 
@@ -52,8 +48,8 @@ G0139, G0151, G0157, G0174, G0167, G0185. Use the same list before and after tra
 
 | Setting | Value |
 |---|---|
-| Model | `~/models/Devstral-Small-2-24B-4bit` (MLX affine 4 bit); after training the same with `--adapter-path` |
-| Thinking | none: Devstral has no thinking mode; no `enable_thinking` is sent. (Qwen: off, fixed; with thinking on, all Qwen runs ended with empty responses at the thinking limit, `runs/archive/qwen38/baseline_thinking_on.json`) |
+| Model | `~/models/Qwen3.8-27B-4bit` (MLX affine 4 bit); after training the same with `--adapter-path` |
+| Thinking | off (`enable_thinking=false` per request); with thinking on, all Qwen runs ended with empty responses at the thinking limit (`runs/archive/qwen38/baseline_thinking_on.json`) |
 | temperature / top_p / top_k / min_p | 0.2 / 0.95 / 20 / 0 |
 | max_tokens per turn | **16384**, sent in each request by `train/baseline.py` (`MAX_TOKENS`); the server limit stays 32768 |
 | Tool-call budget per task | 60 calls, 15 activations (T01 too) |
@@ -68,12 +64,11 @@ T01 test with 32768 tokens and budget 40 (`t01_test_budget40`: 40.0) and the thi
 
 ## Baseline
 
-- Devstral baseline (2026-10-04): `python3 train/baseline.py --label baseline_devstral --run-base 21000`, started by
-  `train/baseline_chain.sh` (stop rule after 4 tasks: all loop and repair rate below 20 % → stop; not triggered).
-  Results `runs/stage1/baseline_devstral.json` (copy: `runs/stage1/baseline.json`), run directories
-  `runs/stage1/baseline_devstral/`, report `runs/stage1/baseline_devstral.md`.
-- Devstral result (not used further): mean 6.8; 1 of 11 tasks above 0 (G0167: 75). Qwen: mean 15.8, 3 of 11. End reasons: loop 6, tool_budget 3, report 2. Repair rate 64/76 = 0.84
-  (the model changes the source, but the changes do not remove the cause).
-- Per run record now also has `pushes_after_error`, `pushes_changed_after_error`, `repair_rate`.
-- The Qwen baseline was never completed (archive: `runs/archive/qwen38/`).
-- The stage 1 training test (step 3) now uses Devstral. `mlx_lm.lora` must be checked for `mistral3` before the test.
+- Official baseline: Qwen 3.8 27B, `python3 train/baseline.py --label baseline_qwen --run-base 22000` (MacBook, thinking off).
+  Results `runs/stage1/baseline.json` (= `baseline_qwen.json`), run directories `runs/stage1/baseline_qwen/`.
+  Mean **15.8**; 3 of 11 tasks above 0 (T01 48.3, G0157 51.0, G0185 75.0). End reasons: loop 7, tool_budget 3, report 1.
+  Repair rate 39/54 = 0.72.
+- Devstral Small 2 (dropped): mean 6.8; 1 of 11 above 0 (G0167: 75); loop 6, tool_budget 3, report 2; repair rate 0.84.
+  Results in `runs/archive/devstral/`.
+- Per run record has `end_reason`, `pushes_after_error`, `pushes_changed_after_error`, `repair_rate`.
+- The stage 1 training test (step 3) uses Qwen (`config_test.yaml`).