JamieBradfield

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

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 ALL training generations (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)	v27	13/20	11/20	11/20	0/20
T1	v28 ckpt-175 (this model)	17/20	16/20	16/20	4/20
T2 (novel multi-step)	v27

Headline: tier-1 held-out fidelity 13→17 fired / 11→16 todos_ok — the strongest held-out result in the v2→v28 lineage. Tier-2 holds at 7/10. The gate is reproducible: run twice (pre-flip and post-flip), identical scores.

Why ckpt-175 and not ckpt-214: the final checkpoint (214) regressed on the tier-3 over-trigger guard (8/10 fired, 6/10 unconditional todo reaches on trivial asks). Root cause: the v28 corpus has zero no-call / just-answer rows, so the model trained past the point where it can decline a tool call. ckpt-175 at epoch ~6.5 is the sweet spot: full tier-1/2 capability, tier-3 still within the v27 bar. This is the "more data ≠ better" lesson, measured in-checkpoint this time.

format_exact caveat (important): the format column demands envelope-only output (^\s*<tool_call>.*?</tool_call>\s*$). v28's low format_exact is the same phenomenon documented in v27: perfect envelopes but entire multi-turn agentic loops in one completion (<tool_call>todo</tool_call> → simulated user <tool_response> → next <tool_call>search_files</tool_call> → …). The live Hermes runtime parses this fine (verified in real sessions); it just fails the envelope-only regex.

Known limitation (no-call / memory reflex): because the corpus has no no-call rows, the model will still reach for a tool on questions that need none (e.g. a trivia ask triggers a tool call rather than a direct answer). It also reaches for tools it was never trained on when the runtime exposes them (observed: memory calls with invented facts on a trivia question; the runtime guardrails rejected them). Use within an agent harness with validation; do not use for open-ended chat without tool-scope enforcement.

The base distill's tool-calling was measured at zero on the synthetic harness only; through the native interface it fires 18/20 (see the Addendum at the top of this card).

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); v29 is in design (RAG via docs-index + clarify, plus a clean-base vs warm-lineage A/B).

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 for `build_system`, `pad_to_bucket`, `conv_hash`, constants)	shared helpers
`mine_v28_tasks.py`	task mining from DJLougen corpus (Jaccard-dedup vs probes + v27 training set)	112 tasks
`analyze_v28_tasks.py`	task-prompt sanity analysis (dedup, leakage, verify-kind)	task stats

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. The sandbox used for generation is hermetic and wiped per run — no real user data entered the training trajectories.

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

Published separately as hermes-fc-v28-data (Apache-2.0): train_v28.jsonl (218 rows), tasks_v28.jsonl (112 tasks), and the harness/build scripts. Teacher: GLM-5.3-Flash via the HuggingFace router; task corpus derived from DJLougen Hermes traces (Apache-2.0). The v28 dataset contains zero traces from the author's own sessions (hermetic sandboxes, synthetic tasks) — which is what makes it publishable where v26/v27 were not.

Acknowledgements

Base model and license inherited from Empero/Qwen3.8-9B (Apache-2.0). Teacher distillation via GLM-5.3-Flash (Zhipu AI) through the HuggingFace router; task corpus derived from DJLougen Hermes traces (Apache-2.0).

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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