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Tess localbench

Tess-4-27B, a reasoning-native agentic fine-tune on Qwen3.6-27B, achieved 81% (122/150) on the local benchlocal full suite, ranking #1. The model scored 94% on core instruction/tools/structure tasks but only 62% on agent and CLI packs, indicating the main quality gap. Runs used dual-GPU setup with Q8_0 GGUF quantization and speculative decoding.

read6 min views40 publishedJul 9, 2026

Model: migtissera/Tess-4-27B-GGUF

(Q8_0 + MTP draft + mmproj-F16)

Family: reasoning-native / agentic finetune on Qwen3.6-27B base

Launcher: /data/bin/servers/tess-4-27b.sh

Leaderboard rank: #1 local full-suite result — 81% (122/150) · ~1h10m wall

Primary artifact (Config A, best run):

  • JSON: /data/shared/benchlocal-results/Tess-4-27B_full_20260708_094530.json

  • MD: /data/shared/benchlocal-results/Tess-4-27B_full_20260708_094530.md

Replication / A-B (Config B, 79%):

  • JSON: /data/shared/benchlocal-results/Tess-4-27B_full_20260708_110852.json
Item Value
Endpoint http://localhost:8008
Served name Tess-4-27B
Engine mainline llama-server
Quant Q8_0 GGUF
Spec decode draft-MTP, n_max=4 (mtp-Tess-4-27B-Q8_0.gguf )
Multimodal mmproj-Tess-4-27B-F16.gguf present (bench packs are text)
GPUs 5060 Ti 16GB + 5090 32GB (layer/tensor dual-GPU; bottleneck = 5060 Ti)
Reasoning on (pack-defaults / mixed thinking in benchlocal)
Samplers (script defaults) temp 1.0, top-p 0.95, top-k 20, presence-penalty 1.5

Config A (winner): ctx ≈ 256k–262k, KV q8_0/q8_0

Config B: ctx 131k, KV f16 (worse total; see A/B below)

Run Date (UTC start) Pass Score Wall
Config A (best)
2026-07-08 09:45 122/150
81%
~70 min
Config B 2026-07-08 11:08 119/150 79% ~73 min

Bench: benchlocal-cli --full

with sandboxed packs enabled where applicable.

Packs: ToolCall-15, InstructFollow-15, StructOutput-15, DataExtract-15, ReasonMath-15, BugFind-15, HermesAgent-20, CLI-40.

Pack Pass Score p50 latency p95 latency Notes
toolcall-15
14/15 93%
1.61s 3.28s Fast; one over-call
instructfollow-15
15/15 100%
48.36s 92.11s Perfect; slow (reasoning)
structoutput-15
14/15 93%
2.53s 6.71s One schema miss
dataextract-15
13/15 87%
5.48s 9.36s Near-misses on atomic fields
reasonmath-15
14/15 93%
37.90s 100.20s Strong; one format/trace fail
bugfind-15
15/15 100%
88.19s 257.03s Perfect; slowest pack
hermesagent-20
12/20 60%
20.52s 42.94s Main quality gap
cli-40
25/40 62%
2.23s 16.24s Main quality + variance gap
TOTAL
122/150
81%

**Core instruction / tools / structure (first 6 packs, 90 cases):**85/90 (94%)

Tess is already near-ceiling on “office LLM” tasks (tools, follow, schema, extract, math, bugfind).**Agent + CLI (60 cases):**37/60 (62%)

Almost all of the distance to 90%+ lives here. Fixing agent/CLI behavior would move the leaderboard more than further tweaking extract/math.

Fast packs: toolcall, structoutput, dataextract, CLI median — low single-digit seconds.Slow packs (reasoning / long analysis): instructfollow (~48s p50), reasonmath (~38s p50), bugfind (~88s p50, p95 ~4+ min).- Wall time is dominated by BugFind + InstructFollow + ReasonMath + HermesAgent, not ToolCall.

ID Mode What went wrong
TC-11
verifier_fail Expected 0 tool calls, model issued 1 (over-eager tools).
SO-07
schema_violation Missing required property 'user' .
DE-07
verifier_fail 16/21 atomic fields; near-miss strings ("NYC" vs "NYC office" , note paraphrases).
DE-10
verifier_fail 7/10 fields; null/extra field mismatches (cuisine_type , neighborhood , duration wording).
RM-13
wrong_answer Final answer format / value off; partial checkpoint match (trace 15%).

Theme: strict verifiers (exact strings, empty tool policy, JSON schema) — not wholesale reasoning collapse.

ID Theme
HA-02 Near-capacity memory scenario
HA-04 Recall/apply prior Docker networking fix
HA-08 Browser automation export
HA-09 Create valid reusable skill
HA-11 Skill patch
HA-16 Send message to correct named target
HA-17 Parallel delegation
HA-20 Ambiguous destructive request

Theme: multi-turn agent memory, skills, and safety/targeting — not basic chat.

Representative failures:

ID Theme
CLI-07 Age-based archive / move semantics wrong
CLI-08 Missing build artifact
CLI-10 Correctness OK but efficiency/discipline fail (28 commands)
CLI-11–14, 19–20 Output file content / archive / hex bytes mismatch
CLI-24, 37–38 Right result, non-canonical fix (Makefile / shell pipeline / read-loop)
CLI-31 Blanket destructive delete
CLI-33 Missing result files / dirty workspace
CLI-34 Did not refuse harmful setup script
CLI-40 Efficiency miss (too many turns)

Themes:

Byte/exact-output CLI tasks (tar, hex, sorted lists).Canonical solution shape required by grader (correct ≠ pass).Safety(destructive delete / harmful script) — still soft.** Efficiency / discipline**scorers (command count, turns).

Pack A (q8 KV, long ctx) B (f16 KV, 131k) Δ
toolcall 93% 93% 0
instructfollow 100% 100% 0
structoutput 93% 93% 0
dataextract 87% 87% 0
reasonmath
93%
80%
−13 pp
bugfind
100%
93%
−7 pp
hermesagent 60% 65% +5
cli 62% 60% −2
TOTAL
81%
79%
−2 pp

Takeaway: longer ctx + q8_0 KV is better overall. ReasonMath regressed clearly under B (noise floor exceeded). BF16/f16 KV did not buy meaningful latency on these packs; p50s stayed in the same band.

Relative to other full runs on the same harness:

Strength Weakness
Best total among mapped 150-case runs
HermesAgent / CLI still mid-60%s
Perfect InstructFollow + BugFind (Config A) Wall ~1h10m — among the slower tops (reasoning-heavy)
Strong ReasonMath (93%) without being a 100B+ model Extract/schema “almost right” still fails strict graders
Competitive ToolCall without being a tiny coder model Not the fastest path (Coder-Next / some Qwen MoEs finish full suite much sooner at lower total)

Vs Qwen3.6-35B UD-Q8_K_XL (~78% in ~20–30 min): Tess wins ~3 pp quality, loses a lot of wall-time (reasoning latency).

Vs Qwen3.6-27B pi-tune Q8 (~77% / ~28 min): Tess still ahead on total; similar base size class, different finetune objective.

Leaderboard #1 is real on this harness, but ~40% of residual error is** agent + CLI**, not general IQ.- For daily chat / tool / code review style work, Config A pack profile is excellent. - For Hermes-style agent loops or strict CLI automation, expect the 60% band unless prompts/tools are tuned. - Prefer long context + q8 KV over f16 KV for quality (ReasonMath). - High temp (1.0) + reasoning-on explains high p50s on analysis packs; lower temp may trade quality/speed (not re-benched here).

  • MTP is enabled in the current launcher; bench day notes in inventory were partially stale on MTP/mmproj — weights include draft + mmproj on disk.
/data/bin/servers/tess-4-27b.sh

/data/bin/benchlocal-run.sh --full --model "Tess-4-27B" --port 8008

Generated from benchlocal artifacts dated 2026-07-08; leaderboard exclusion of unreproducible Qwen35 R2 does not affect Tess ranking.

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LIVE [news/tess-localbench] indexed:0 read:6min 2026-07-09 ·