Use Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "tcclaviger/Tess-27B-RFA" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "tcclaviger/Tess-27B-RFA",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'All-RFA 4-bit quant of migtissera/Tess-4-27B
Runtime: requires
tcclaviger/vllm:latest— an RDNA 4 (gfx12xx) vLLM image and the only build with the RFA kernels; no other vLLM build loads these weights. Not validated on any other hardware at this time.
Tess-27B-RFA
Aggressive all-RFA 4-bit quantization of Tess-4-27B by Migel Tissera — an agentic, thinking-native finetune of Qwen3.6-27B. Unlike the sibling Tess-27B-RFI (int8 attention), this build runs every attention path at 4-bit: full attention, GDN linear attention, and even the MTP speculative head's attention. All credit for the model to its author; this repo only changes the numerics.
Quantization by component
- All attention (full self_attn + GDN linear attention) — 4-bit float weights (RFA): IQ4_NL non-linear grid, group size 16, asymmetric, Hadamard-16 rotation, block-float scales stored as int8 mantissa + int8 exponent.
- MLP layers — same RFA 4-bit scheme.
- MTP speculative-decode head — its attention projections are RFA 4-bit too; its fc and MLP stay bf16.
- Kept in bf16 (not quantized) — vision encoder, GDN
in_proj_a/in_proj_b, embeddings, norms, and the lm_head.
Result: 20.5 GB (vs 55.6 GB bf16, 28.9 GB RFI) at essentially zero measured quality cost.
Serving context — 512K via YaRN
All evaluation below was run while serving at --max-model-len 524288 (512K tokens),
extended from the native 256K window with YaRN via --hf-overrides:
{"text_config": {"rope_parameters": {"rope_type": "yarn", "factor": 2.0,
"original_max_position_embeddings": 262144, "mrope_interleaved": true,
"mrope_section": [11, 11, 10], "partial_rotary_factor": 0.25,
"rope_theta": 10000000}}}
Evaluation results
Six builds measured with identical methodology, each against its own live vLLM endpoint (July 2026): Qwen3.6-27B (bf16 base) → Tess-4-27B (the tune, bf16) → Tess-27B-RFI (int8+4-bit hybrid) → Tess-27B-RFA (this build, all-4-bit) → Tess-FP8 (W8A8 block-128 FP8 sibling), with Qwen3.6-35B-A3B (MoE, bf16) as a comparative reference. Bold marks the best score in each row (ties all bolded).
Quantization cost (all-RFA 4-bit) — Tess-4-27B → Tess-27B-RFA
| Metric | Change |
|---|---|
| Checkpoint size | 55.6 → 20.5 GB (−63%) |
| WikiText-2 perplexity | 6.669 → 6.629 (−0.6%, better) |
| Codeneedle overall recall | 97.7% → 98.1% (best of family) |
| MC accuracy (4 tasks) | ≈ flat (−0.6 to +1.3 pp) |
| Tool-eval / GSM8K / MMLU / IFEval | 86 / 98% / 80% / 95% |
| MTP acceptance length | ~2.9 → ~2.8 (≈ flat) |
| Decode @ conc 1 (ISL 128) | 59.9 → 67.9 tok/s (+13%) |
| Decode @ conc 50 (ISL 128) | 528 → 423 tok/s (−20%) |
Quality
| Metric | Qwen3.6-27B (base) | Tess-4-27B | Tess-27B-RFI | Tess-27B-RFA | Tess-FP8 | Qwen3.6-35B-A3B |
|---|---|---|---|---|---|---|
| WikiText-2 PPL (n_ctx 2048, lower is better) | 7.0559 | 6.6691 | 6.6632 | 6.6292 | 6.6627 | 6.5092 |
| ARC-Challenge (acc_norm) | 59.30% | 60.84% | 60.41% | 60.32% | 60.49% | 55.20% |
| ARC-Easy (acc_norm) | 75.93% | 77.53% | 77.40% | 78.87% | 77.82% | 71.13% |
| Winogrande (acc) | 77.51% | 77.43% | 77.51% | 76.80% | 77.66% | 73.40% |
| HellaSwag (acc_norm) | 84.12% | 84.21% | 84.27% | 84.05% | 84.13% | 82.95% |
Multiple-choice accuracy is lm-eval loglikelihood scoring, 0-shot.
Long-context positional recall (codeneedle)
Verbatim function recall under 10K–80K-token contexts.
| Corpus | Qwen3.6-27B (base) | Tess-4-27B | Tess-27B-RFI | Tess-27B-RFA | Tess-FP8 | Qwen3.6-35B-A3B |
|---|---|---|---|---|---|---|
| Python | 100% | 100% | 100% | 100% | 99.55% | 99.09% |
| C++ | 98.12% | 98.12% | 98.44% | 98.75% | 98.75% | 98.44% |
| Rust | 99.69% | 99.69% | 99.69% | 99.69% | 99.69% | 99.38% |
| JS (~80K tokens) | 93.44% | 93.13% | 93.75% | 93.75% | 93.44% | 92.19% |
| Tools | 98.26% | 99.57% | 99.57% | 99.57% | 99.57% | 93.48% |
| Overall recall | 97.81% | 97.73% | 97.97% | 98.05% | 97.86% | 97.28% |
Tool calling & accuracy benches
| Bench | Qwen3.6-27B (base) | Tess-4-27B | Tess-27B-RFI | Tess-27B-RFA | Tess-FP8 | Qwen3.6-35B-A3B |
|---|---|---|---|---|---|---|
| tool-eval final (full 69, TC-61 excl) | 86 | 85 | 87 | 86 | 87 | 90 |
| GSM8K (50q) | 98.0% | 94.0% | 98.0% | 98.0% | 98.0% | 96.0% |
| MMLU (50q) | 74.0% | 76.0% | 82.0% | 80.0% | 76.0% | 64.0% |
| IFEval (20 prompts, prompt-level) | 90.0% | 90.0% | 90.0% | 95.0% | 90.0% | 90.0% |
Decode throughput — tok/s output (ISL 128 / ISL 512)
vllm bench serve, random dataset, OSL 128, saturation, 4× R9700 (gfx1201), TP 4.
| Concurrency | Qwen3.6-27B (base) | Tess-4-27B | Tess-27B-RFI | Tess-27B-RFA | Tess-FP8 | Qwen3.6-35B-A3B |
|---|---|---|---|---|---|---|
| 1 | 57.0 / 61.4 | 59.9 / 63.8 | 75.3 / 69.3 | 67.9 / 58.2 | 86.0 / 85.1 | 91.9 / 114.4 |
| 10 | 304.5 / 233.6 | 289.6 / 227.8 | 281.5 / 219.2 | 292.2 / 194.8 | 280.6 / 269.2 | 434.9 / 440.3 |
| 25 | 424.4 / 321.7 | 492.0 / 341.1 | 429.1 / 284.0 | 369.0 / 260.3 | 533.2 / 355.4 | 688.9 / 563.7 |
| 50 | 556.0 / 349.8 | 527.9 / 345.6 | 452.8 / 295.0 | 422.6 / 278.4 | 560.3 / 394.1 | 889.8 / 702.9 |
MTP draft acceptance by work category
Measured from live serving logs, k=5 draft tokens, drafted-token-weighted aggregation.
| Work category | Overall acceptance | Pos 1 | Pos 2 | Pos 3 | Pos 4 | Pos 5 |
|---|---|---|---|---|---|---|
| JSON generation | 87.3% | 96.4% | 92.2% | 86.8% | 82.9% | 78.0% |
| Math | 78.2% | 95.6% | 87.7% | 78.6% | 68.8% | 60.2% |
| Code | 63.4% | 89.4% | 74.9% | 60.7% | 49.8% | 41.9% |
| Creative English | 63.1% | 88.0% | 73.3% | 61.6% | 50.1% | 42.3% |
Notes
All builds serve on the tcclaviger/vllm:latest image, which has kernel tunes baked in. TunableOp is untuned — GEMMs run on default heuristic-determined values. Base Qwen3.6-27B figures are the 2026-07-12 re-measurement on the same tcclaviger/vllm:latest image and thinking-OFF methodology as every other build, replacing an earlier non-comparable run.
Credits
Tess-4-27B by Migel Tissera (migtissera/Tess-4-27B) — the model quantized here:
@misc{tissera2026tess4, title = {Tess-4-27B}, author = {Migel Tissera}, year = {2026}, howpublished = {\url{https://huggingface.co/migtissera/Tess-4-27B}} }codeneedle (positional recall) originally by Alexander Ziskind, expanded test suite by tcclaviger (Rob Smith).
Tool-calling scenarios (incl. TC-61) run on tool-eval-bench by SeraphimSerapis (Tim Messerschmidt), scenario methodology adapted from ToolCall-15 by stevibe.
GSM8K, MMLU, and IFEval run via tool-eval-bench's built-in accuracy benchmarks at their defaults: GSM8K 8-shot CoT, MMLU 5-shot, IFEval zero-shot.
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tcclaviger/Tess-27B-RFA" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tcclaviger/Tess-27B-RFA", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'