JamieBradfield

qwen3.8-9b-hermes-fc-todo

README

License: apache-2.0

Addendum 2026-09-03 — evaluation-methodology correction

Read this before you use the numbers below.

After this checkpoint was published, we tested the base model (Empero/Qwen3.8-9B-Distill, no fine-tuning) through the live Hermes interface: the OpenAI-native tools parameter path that Hermes actually uses. The base model calls tools correctly on its own.

The harness that certified this checkpoint did not use that interface. It baked tool schemas into the system text, never passed tools, and required raw <tool_call> XML in the response. That prompt shape matches neither the base model's own chat template nor the live Hermes request path. Every gate score in the v26-to-v29 lineage measured this synthetic interface.

The statement in this README that "the base distill contributes zero tool-calling" is incorrect. It measured the synthetic interface.

Corrected comparison (native tools path, same 4-tool battery, greedy):

Table with columns: model, tier-1 fired, tier-1 name=todo, tier-2 todo-first, tier-3 fired (want 0), tier-4 fired (want 0)
model	tier-1 fired	tier-1 name=todo	tier-2 todo-first	tier-3 fired (want 0)	tier-4 fired (want 0)
base (no fine-tune)	18/20	17/20	3/10	5/10	0/10
v28 ckpt-175 (this model)	17/20	10/20	8/10	9/10	4/10
armB2-175 (clean retrain)	10/20	8/20	10/10	4/10	0/10

What the fine-tune lineage actually changed: todo-first planning on multi-step tasks (tier-2 name=todo 3/10 base to 8/10 v28 to 10/10 armB2). That is real, and it is the one capability the lineage added. The costs: restraint regressions (tier-3 over-trigger 5/10 to 9/10; tier-4 tool-drift on trivia 0/10 to 4/10) and, in armB2, over-suppression (tier-1 10/20).

Status: the fine-tune program is frozen as of 2026-09-03. The base model is the author's runtime default. This checkpoint stays published as the strongest measured demonstration of SFT-taught todo-first planning in the lineage, and as the artifact that exposed the harness error. The Evaluation section below describes behavior on the synthetic interface; do not read it as native-interface competence.

Reproduction: scripts/native_battery.py + scripts/todo_probes.jsonl

scripts/no_drift_probes.jsonl produce the corrected table (any GGUF, CTX=65536 to match the serve config).

Base model: Empero/Qwen3.8-9B (Apache-2.0)
Warm start: v27 merged weights (qwen3.8-9b-hermes-fc-todo), QLoRA adapter on top (envelope + tool tokens already baked in)
Training data: 218 trajectories (ShareGPT format, 551 todo calls), generated by GLM-5.3-Flash via the HuggingFace router on 112 synthetic tasks mined from the author's agent corpus — not the author's own traces:
- 190 teacher trajectories (85% verified by sandbox execution; failures with recovery injected for 15.8% of rows)
- 61 rows carry distilled short-chain-of-thought; 159 envelope-only
- 12 synthetic web-search tasks (first web signal)
- Full dataset published separately (see hermes-fc-v28-data); probes were kept virgin (zero overlap with generation or training)
Method: QLoRA 4-bit, rank 16 / alpha 16, dropout 0, batch 1 x grad-accum 8 (effective 8), lr 2e-4, warmup 0.1, MAX_SEQ 6144, 8 epochs = 214 steps (final loss 0.02085, avg train loss 0.09405)
248077 → 248079 (2 added tool tokens: , )

Evaluation (todo-probe battery, greedy, todo schema in every prompt)

40 probes, 3 tiers: Tier 1 = 20 real DJLougen todo trajectories held out of training (hash-exact, zero leakage; GT = first todo call). Tier 2 = 10 synthetic multi-step tasks (refactor+tests+docs, CI setup, release prep) → autonomous todo reach-for. Tier 3 = 10 trivial asks (want 0/10) → over-trigger guard.

Table with columns: tier, model, fired, name=todo, todos_ok, format_exact
tier	model	fired	name=todo	todos_ok	format_exact
T1 (real, held-out)	v26	13/20	10/20	10/20	6/20
T1	v27 (this model)	13/20	11/20	11/20	0/20
T2 (novel multi-step)	v26

Headline: Tier-1 held-out fidelity held (13/20 = 13/20, name 10→11), and tier-2 autonomous todo reach-for moved 3→7 name=todo / 1→7 todos_ok — the swing metric. 393 SFT demos taught a novel multi-step task to reach for the todo tool without any RLVR. Tier-3 over-trigger improved 7→5 but is not zero (remaining fires are mostly other tools on trivial asks — generic tool-calling eagerness, not todo-specific).

format_exact caveat (important): the format column demands envelope-only output (^\s*<tool_call>.*?</tool_call>\s*$). v27 scores 0/20 on tier 1 not because its envelopes are malformed — full-output inspection shows it emits perfect envelopes but entire multi-turn agentic loops in one completion (<tool_call>todo</tool_call> → simulated

markdown

user <tool_response>

→ next <tool_call>search_files</tool_call> → …). That is the DJLougen validate→mutate→verify curriculum behaving as intended, and the live harness parses it fine; it just fails the envelope-only regex.

The base distill contributes zero tool-calling (0/45 old eval); the entire capability comes from the fine-tune.

Intended use

Research artifact for experimenters working on tool-call behavior in 9B-class models — not a product. The tool_call XML envelope and tool schemas match the Hermes agent runtime this was trained on. This is an intermediate milestone in an iterative recipe series (v1 → v2 → v25 → v26 → v27 → v28); v28 is the target default.

What this repo contains

The BF16 merged weights (this repo)
All scripts that produced the model and dataset (scripts/)

Reproducibility

All scripts that produced this model are in scripts/:

Table with columns: file, purpose, number it produced
file	purpose	number it produced
`build_v26.py`	base dataset build (imported by v27 builders for `build_system`, `pad_to_bucket`, `conv_hash`, constants)	shared helpers
`build_v27_todo_real.py`	DJLougen trace extractor: event-stream parse, think-block strip, inline tool_call/tool_response split, todo truncation bypass, windowing → 72 rows	`todo-real` band
`build_v27_condensed.py`	same 72 rows with ~90-token system stub → 72 rows

The scripts carry the author's machine paths (F:/models/..., C:/AI/...) and run on this rig's ROCm stack (Unsloth 2026.8.18, torch 2.11 ROCm, RX 7700 XT); adjust paths for your environment.

Convert/quantize: llama-rocmfpx fork, convert_hf_to_gguf.py --outtype bf16 → llama-quantize Q4_0_ROCMFP4_FAST (see GGUF repo).

Quantized version

Q4_0_ROCMFP4_FAST GGUF (ROCmFPX format, AMD RDNA3 kernels) is published in the companion repo. ROCmFPX quants target AMD ROCm inference; for portable use, convert from the BF16 merge here.

Training data note

The training data itself is not published. The rows derive from the author's own agent sessions plus DJLougen Hermes traces (Apache-2.0), and some rows contain private strings (hostnames, session identifiers). The scripts that build the dataset are published; the data is not. The weights do not contain those strings.

Acknowledgements

Base model and license inherited from Empero/Qwen3.8-9B (Apache-2.0). Training data derived from the author's own Hermes agent sessions plus publicly-licensed teacher sources (SWE-rebench, APIGen-MT-5k, When2Call, DJLougen/hermes-agent-traces-filtered).

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Model Details

Model Provider

JamieBradfield

Model Tree

Base

empero-ai/Qwen3.8-9B-Distill

Fine-tuned

this model

Input Modalities

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Output Modalities

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Supported Functionality