Instructions to use libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX 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("libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX") 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 libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX"
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": "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX 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 "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX"
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 libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX"
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 "libertydesignstudio/OverSeer-Qwen3.8-27B-MTPLX" \ --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"
OverSeer Qwen 3.8 27B MTPLX
A mixed-precision MLX build of Qwen/Qwen3.8-27B with its native MTP draft head preserved for speculative decoding through MTPLX.
The release target is a 24 GB Apple Silicon workstation: approximately the memory footprint of a Q3_K_M GGUF while materially improving decode speed.
Measured result
Tested on a base Apple M4 with 24 GB unified memory, macOS 26.5.2, MLX 0.32.0, mlx-lm 0.31.3, and MTPLX 2.7.2.
| Measurement | Result |
|---|---|
| Loaded model weights | 13.4 GB |
| Forge D2 verification | 13.79 tok/s |
| OpenAI server, two 300-token prompts | 11.23–14.01 tok/s |
| Long reasoning samples | 10.95–13.08 tok/s |
| Same-artifact AR baseline | 7.41 tok/s |
| Prior LM Studio Ridge baseline | 5.26–5.79 tok/s |
The server measurements used performance-cold, MTP depth 2, one active request, a one-entry/1 GB session bank, and extended background warmup disabled. Generation speed excludes prompt prefill; end-to-end latency still depends on prompt length.
Quality checks
The release candidate was checked on three paired tasks:
- Levenshtein edit-distance implementation: executable code passed five deterministic cases; stated
O(m·n)time and space. - TCP versus UDP explanation: correct connection, reliability, ordering, and analogy requirements.
- First-occurrence binary-search repair: executable code passed seven boundary and duplicate cases.
Known limitation: an optional illustrative edit-distance table contained incorrect intermediate cells, although both generated implementations, the complexity analysis, and the final distance were correct. The debugging task needed a 4,096-token reasoning allowance to reach its correct final answer; 1,800 tokens ended during reasoning. This is a small release check, not a broad quality benchmark.
Quantization
The body defaults to 2-bit affine quantization with higher precision on quality-sensitive paths:
- embeddings and LM head: 6-bit
- linear-attention QKV/Z/out: 4-bit
- linear-attention A/B: 8-bit
- full-attention Q/K/V: 5-bit; output: 6-bit
- all MLP down projections: 3-bit
- first and last four MLP gate/up projections: 3-bit
- native MTP sidecar: BF16
The complete machine-readable recipe is in quantization_recipe.json.
Forge verification patch
The included MTPLX 2.7.2 patch is needed to reproduce Forge's MTP-depth verification for this 2-bit body. The recommended server intentionally leaves MTPLX_NAX_VERIFY unset: enabling that experimental dispatcher reduced decode on an 8.1K-token OMP prompt from 10.23 to 2.94 tok/s. Stock MLX quantized matmul is the validated server lane.
python -m pip install 'mtplx==2.7.2'
SITE_PACKAGES="$(python -c 'import site; print(site.getsitepackages()[0])')"
patch -p1 -d "$SITE_PACKAGES" < mtplx-2.7.2-q2-verify.patch
The patch adds numerically checked 2-bit affine verify kernels for MTP row counts 2 through 6. Synthetic BF16 comparisons against stock MLX measured maximum absolute differences from 0.00225 to 0.00384 across those row counts.
Recommended text server
MTPLX_SESSION_BANK_MAX_ENTRIES=1 \
MTPLX_SESSION_BANK_MAX_BYTES=1G \
MTPLX_SESSION_BANK_PER_SESSION_BYTES=1G \
MTPLX_SESSION_BLOCK_PREFIX_RESTORE=0 \
MTPLX_MEMORY_PRESSURE_GUARD=0 \
MTPLX_WARMUP_EXTENDED=0 \
mtplx serve \
--model ./OverSeer-Qwen3.8-27B-MTPLX \
--profile performance-cold \
--depth 2 \
--scheduler-mode serial \
--batching-preset solo \
--max-active-requests 1
performance-cold is the measured fast lane for text requests under 8K prompt tokens (10.95–14.01 tok/s). Native OpenAI function calling works on a compact 604-token direct request (one valid call at 10.96 tok/s), but the full OMP tool/rule prompt expands to roughly 23.7K tokens: performance-cold correctly blocks that unsafe prefill, while sustained did not complete the end-to-end OMP agent turn within 15 minutes. This release is therefore recommended as a fast text/coding model, not as the default full-tool agent. Upstream model metadata retains 262K context; the qualified local OMP text route advertises 16K.
Files
- mixed-precision MLX language-model shards
mtp.safetensors: native BF16 MTP headmodel-vision.safetensors: restored vision towermtplx_runtime.json: runtime contract and measured verification evidencequantization_recipe.json: reproducible Forge recipemtplx-2.7.2-q2-verify.patch: optional Forge/tuning verification patch
License
Apache 2.0, matching the upstream Qwen3.8-27B release. This derivative is not affiliated with or endorsed by the Qwen team.
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