Instructions to use nom666/Qwopus3.8-27B-MTPLX-8bit-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nom666/Qwopus3.8-27B-MTPLX-8bit-Quality with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("nom666/Qwopus3.8-27B-MTPLX-8bit-Quality") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use nom666/Qwopus3.8-27B-MTPLX-8bit-Quality with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use nom666/Qwopus3.8-27B-MTPLX-8bit-Quality with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use nom666/Qwopus3.8-27B-MTPLX-8bit-Quality with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default nom666/Qwopus3.8-27B-MTPLX-8bit-Quality
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nom666/Qwopus3.8-27B-MTPLX-8bit-Quality with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "nom666/Qwopus3.8-27B-MTPLX-8bit-Quality" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Qwopus3.8-27B · MTPLX 8-bit Quality
The near-lossless MTPLX build of Qwopus3.8-27B — a task-vector transplant that carries the Qwopus reasoning distillation onto the Qwen3.8-27B base. Qwopus3.8 keeps Qwen3.8's architecture, context length (262,144) and native MTP speculative head, and layers the Opus-style distilled reasoning trait on top: materially fewer tokens for the same result, and a stronger non-thinking (instruct) mode. Speed-focused sibling: Qwopus3.8-27B-MTPLX-4bit-Speed.
How it was made
Qwen3.6-27B and Qwen3.8-27B share an identical qwen3_5 skeleton (64 layers, 5120 hidden, 248K vocab, hybrid GDN + full attention, 1-layer MTP head). That makes cross-generation task arithmetic shape-exact:
Qwopus3.8 = Qwen3.8 + (Qwopus3.6-27B-v2 − Qwen3.6)
- All 1,199 tensors matched and merged (fp32 math, bf16 out) — including the
mtp.*speculative head and the vision tower; zero skipped, zero shape mismatches. - Forged with MTPLX: body 8-bit (group 64, affine); MTP head kept bf16.
- The delta-merged MTP head verifies above the donor artifacts: acceptance 96.0% / 90.7% / 82.7% at draft depths 1–3.
Results (vs Qwen3.8-27B 8-bit, same harness, Apple M5 Max)
Non-thinking mode:
| Qwopus3.8 8-bit (this build) | Qwen3.8 8-bit | |
|---|---|---|
| 38-task instruct/code/math suite | 36/38 | 35/38 |
| Instruction-following subset | 3/5 | 2/5 |
| Hard-task suite (executed code + math + logic) | 8/13 @ 2,889 tok | 8/13 @ 3,384 tok |
| Decode @ d0 (turbo) | 49.6 tok/s | ~49 tok/s |
Non-thinking Qwopus3.8 answers with ~15% fewer tokens at equal hard-task success, and holds instruction-following better than the base model does with thinking disabled. The 4-bit sibling scored 37/38 (instruction-following 4/5) on the same non-thinking suite.
Thinking mode — token efficiency: at high reasoning effort this build matches the base on a 13-task hard suite (13/13 both) while spending 10,050 vs 18,455 reasoning tokens — 45% fewer. The efficiency trait holds on every jointly-solved task of a harder adversarial set (e.g. 11.6K vs 18.0K tokens on a combinatorial coding task). Long-haul agentic tool-chains at 30K/60K/100K-token context ran clean (goal reached, zero repeated calls, zero drift).
Usage
brew install youssofal/mtplx/mtplx
mtplx pull nom666/Qwopus3.8-27B-MTPLX-8bit-Quality
mtplx quickstart --model nom666/Qwopus3.8-27B-MTPLX-8bit-Quality \
--reasoning off --profile turbo \
--temperature 0.3 --top-p 0.9 --top-k 40
For thinking mode, use --reasoning on --reasoning-effort low (or medium) with a generous max_tokens. Do not greedy-decode in thinking mode (a known Qwen-family failure pattern; temperature 0 makes reasoning termination deterministic-worst-case).
Notes
- Recommended for instruct-style serving and agentic/coding loops with reasoning off, or thinking-mode use where reasoning-token economy matters.
- With thinking enabled, Qwopus3.8 solves hard tasks with ~45% fewer reasoning tokens, though base Qwen3.8 remains the stronger choice for heavy thinking-mode workloads — it is more consistent at concluding very long reasoning inside tight token budgets (Qwopus3.8 can occasionally deliberate past a tight budget on some prompts) and keeps an edge on the hardest reasoning tasks.
- This is a weight-space merge, not a fine-tune: no gradient training was performed on Qwen3.8.
- All credit for the Qwopus distillation to Jackrong (Qwopus3.6-27B-v2); base model Qwen3.8-27B by the Qwen team; MTPLX runtime by Youssofal.
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8-bit
Model tree for nom666/Qwopus3.8-27B-MTPLX-8bit-Quality
Base model
Qwen/Qwen3.8-27B