🧰 1. Files & comparison

Head-to-head vs Qwopus3.6-27B-Coder

Agentic coding across quants (SWE-rebench)

Every quant run as a coding agent (mini-swe-agent) over the same 10 held-out SWE-rebench instances, one clean Docker container each. pass_rate = fraction whose patch makes the gold FAIL_TO_PASS tests pass; patch_rate = fraction that produced a non-empty diff. Token/step counts are per instance.

Same 10-instance holdout as the head-to-head, no speculative decoding. At 2-bit the quants stay surprisingly capable agents; higher-bit rows trade size for headroom. Only one repetition is reported above and uncertainties on the pass rate can vary ~5-10% due to sampling variance.

⚠️ These agentic numbers were measured on the pre-recalibration quants (see the "Recalibrated" note in §1). General-eval fidelity is unchanged by the recalibration, so they remain broadly indicative, but a fresh agentic run on the recalibrated GGUFs has not yet been done.

🔬 2. How they were made

🚀 3. Usage

Quick start with Ollama

ollama run hf.co/pearsonkyle/tmax-27b-imatrix-MTP-GGUF:IQ2_M
# also: :Q2_K_S  ·  :IQ2_XS  ·  :Q2_K  ·  :IQ3_M  ·  :IQ4_XS  ·  :Q5_K_M

Building llama.cpp from source (GPU)

apt-get update && apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON   # -DGGML_CUDA=OFF for CPU/Metal
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-server
cp llama.cpp/build/bin/llama-* llama.cpp/

MTP needs a recent llama.cpp — --spec-type draft-mtp was merged in 2026-06. Build from current master.

Running the server with MTP speculative decoding

./llama-server \
    --model tmax-27b-IQ4_XS.gguf \
    --ctx-size 16384 --n-gpu-layers 999 \
    --spec-type draft-mtp --spec-draft-n-max 1 \
    --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 \
    --host 0.0.0.0 --port 1234

Drop the two --spec-* flags to run without MTP. --spec-draft-n-max 1 is optimal (one nextn layer). If the model emits `` reasoning, also pass --chat-template-kwargs '{"enable_thinking": false}' so the answer lands in content.

Querying via the OpenAI-compatible API

import json, urllib.request

def ask(content, max_tokens=256):
    body = {
        "messages": [{"role": "user", "content": content}],
        "max_tokens": max_tokens,
        # tmax may emit  reasoning. Set enable_thinking False
        # (or raise max_tokens) so the answer lands in "content".
        "chat_template_kwargs": {"enable_thinking": False},
    }
    req = urllib.request.Request("http://127.0.0.1:1234/v1/chat/completions",
                                 json.dumps(body).encode(),
                                 {"Content-Type": "application/json"})
    return json.loads(urllib.request.urlopen(req).read())["choices"][0]["message"]["content"]

print(ask("Write a Python function that reverses a linked list."))

🪪 4. License & attribution

• License: Apache-2.0 — inherited from the base model allenai/tmax-27b. Ai2 asks that use follow its Responsible Use Guidelines.
• Base weights: allenai/tmax-27b (Qwen3.6-27B derivative; released checkpoint is the text trunk only — vision + MTP head dropped).
• MTP draft head grafted from: pearsonkyle/Qwopus3.6-27B-Coder-imatrix-2bit-MTP-GGUF (also a Qwen3.6-27B finetune; same head it ships natively). Also the head-to-head baseline.
• Calibration + quantization performed locally with Quant-Tuner.