Qwopus3.6-27B-Coder-MTP-GGUF
Qwopus3.6-27B-Coder is a 27-billion-parameter open-source language model fine-tuned for coding and agent tasks. It builds on Qwopus3.6-27B-v2 (itself a reasoning-enhanced version of Qwen3.6-27B) and adds specialized training for code generation, tool calling, debugging, and multi-turn agentic workflows. The model reports 67.0% on SWE-bench Verified (500 tasks, thinking-off mode). It is distributed as GGUF (quantized format) under Apache 2.0, is ungated, and has ~304k downloads. The developer warns this is an experimental community release not yet fully safety-evaluated across broad domains.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Jackrong |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 304.1k |
| Likes | 324 |
| Last updated | 2026-06-24 |
| Source | Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF |
What Qwopus3.6-27B-Coder-MTP-GGUF is
Dense 27B transformer trained via Trace Inversion (reasoning trajectory reconstruction from Claude Opus data), LoRA/SFT refinement, and agent trajectory learning. Base is Qwen3.6-27B (Alibaba); specialization layers target agentic code generation, structured tool calling, and repository-level reasoning. GGUF quantization enables single-GPU deployment. Achieved 87.43% MMLU-Pro and 75.25% SWE-bench on v2 base; Coder variant reports 67.0% SWE-bench Verified (500 tasks, no-thinking mode, Q5_K_M quantization). Context length, exact parameter count, and tokenizer details not explicitly stated. Training data includes Claude-opus synthetic reasoning traces and agent-reasoning datasets.
Run Qwopus3.6-27B-Coder-MTP-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
27B dense model. ESTIMATE: Q5_K_M quantization ~18–22 GB VRAM (based on typical 5-bit GGUF overhead). Full precision (bfp16) would require ~54 GB. Single A100 (80GB), dual RTX 6000 (48GB ea.), or CPU with 64+ GB RAM + appropriate quantization. Inference speed on GPU ~5–15 tokens/sec (unverified; depends on hardware, quantization, and batch size). Exact tokenizer context length not stated in data provided.
Model card mentions LoRA and SFT in tags and training stack. GGUF is a quantized inference format; full fine-tuning would require loading/converting to native weights (bfp16/fp32). LoRA adapters for domain-specific code or tool-use patterns are plausible if base weights (not GGUF artifact) are available. Unsloth is referenced in tags; this suggests fast fine-tuning harnesses may be available. Requires evaluation of whether full weights are published separately from GGUF.
When to avoid it — and what to weigh
- Guaranteed production safety & compliance — Model card explicitly states 'not undergone full safety evaluation.' Avoid for regulated industries (healthcare, finance, legal) without additional safety vetting and domain-specific testing.
- Low-latency real-time inference at scale — 27B model size and GGUF quantization imply higher latency than smaller 7B models or pruned variants. Requires careful hardware planning if sub-100ms response times are critical.
- Guaranteed multilingual coding parity — Tags list 6 languages (en, zh, es, ru, ja) but SWE-bench (English) is the only completed benchmark. Code performance in non-English codebases or mixed-language repos remains unknown.
- Commercial deployment without legal review — Apache 2.0 is permissive, but model is experimental/community-released and unaudited for commercial liability or data leakage. Requires legal/compliance sign-off before production use in revenue-critical systems.
License & commercial use
Apache 2.0 (https://www.apache.org/licenses/LICENSE-2.0). Clear OSI-approved permissive license. Permits commercial use, modification, distribution, and private use with attribution and no-warranty disclaimer. No restrictions on model output or commercial applications stated in license text.
Apache 2.0 permits commercial deployment without license restrictions. However: (1) model is explicitly experimental/community-release and not safety-audited, creating liability risk if harm or data leakage occurs; (2) base model (Qwen3.6-27B) is from Alibaba (verify Alibaba's own commercial terms apply transitively); (3) training data includes Anthropic's Claude-opus synthetic traces and other datasets—verify downstream licensing of training data is compatible with your commercial intent. Recommend legal/compliance review before revenue-critical deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Standard LLM risks apply: (1) model may generate plausible but incorrect code or hallucinated package imports; (2) agentic tool-calling variant increases risk of unintended function execution if tool definitions/constraints are misconfigured; (3) training data includes synthetic Claude-Opus traces and agent trajectories—potential for subtle prompt-injection or jailbreak patterns to be present; (4) no mention of red-teaming, adversarial testing, or safety filtering. On-premise deployment avoids cloud-provider data exposure but places security responsibility on operator. Always sandbox tool execution and validate model outputs before deployment.
Alternatives to consider
DeepSeek-Coder-33B (or DeepSeek-V3)
Larger open coder model with stronger SWE benchmarks; better long-context (4k–32k); similar Apache/MIT licensing. Trade-off: higher compute cost, less reasoning-trajectory transparency.
Qwen2.5-Coder (32B, 7B variants)
Official successor from Alibaba; likely more stable, broader benchmarking, native long-context support. Trade-off: newer, may have less community adoption; Qwopus is experimental/community-optimized.
CodeLlama-70B or Llama 3 Instruct (70B)
Proven production code models with broad deployment experience and strong general-instruction-following. Trade-off: not specialized for agentic tool-use; larger compute footprint; less reasoning-trajectory augmentation.
Ship Qwopus3.6-27B-Coder-MTP-GGUF with senior software developers
Qwopus3.6-27B-Coder is a strong candidate for research, on-premise code assistance, and agent automation. Pair it with llama.cpp or Ollama for quick local deployment, or use our engineering services to integrate it into your SWE toolchain. Consult legal/compliance before production use.
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Qwopus3.6-27B-Coder-MTP-GGUF FAQ
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Custom software development services
DEV.co helps companies turn open-source tools like Qwopus3.6-27B-Coder-MTP-GGUF into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to Deploy an Open Agentic Code Model?
Qwopus3.6-27B-Coder is a strong candidate for research, on-premise code assistance, and agent automation. Pair it with llama.cpp or Ollama for quick local deployment, or use our engineering services to integrate it into your SWE toolchain. Consult legal/compliance before production use.