Instructions to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit 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("Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit")

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

How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit 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 "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"

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 Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit"

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 "Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit" \
  --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"

Qwopus3.6-27B-Coder-mlx-6Bit / README.md

SolsticeAI

docs: update chat template with <think> reasoning tokens

48ad35a verified about 1 month ago

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	---
	language:
	- en
	- zh
	license: apache-2.0
	tags:
	- anvil
	- turboquant
	- mlx
	- mlx-6bit
	- apple-silicon
	- coding
	- qwen3.6
	- claude-opus
	- trace-inversion
	- agentic-coding
	- reasoning
	- metal
	pipeline_tag: text-generation
	---

	<p align="center">
	<img src="https://cdn-uploads.huggingface.co/production/uploads/67c2e844e0921a5410eec10a/Y5M42dCag2f7Fc6fDtV0Z.jpeg" alt="Solstice-AI Banner" width="100%">
	</p>

	<h1 align="center">Qwopus3.6-27B-Coder (MLX 6-Bit)</h1>

	<h3 align="center">Apple Silicon 6-Bit Native Quantization of Jackrong's Claude Opus-Distilled Qwopus 3.6 Coder</h3>

	<p align="center">
	<img src="https://img.shields.io/badge/org-Solstice--AI-blueviolet" alt="Solstice-AI">
	<img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License">
	<a href="https://github.com/Solstice-Labs/anvil"><img src="https://img.shields.io/badge/engine-Anvil%20Runtime%20(Metal)-crimson" alt="Anvil Runtime"></a>
	<img src="https://img.shields.io/badge/format-MLX%206--Bit%20Affine-orange" alt="Format">
	<img src="https://img.shields.io/badge/context-262K%20Tokens-success" alt="Context Window">
	<img src="https://img.shields.io/badge/focus-Agentic%20Coding%20%26%20Tool%20Use-brightgreen" alt="Focus">
	</p>

	---

	## Executive Summary

	`Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit` is an Apple Silicon-native 6-bit quantization of Qwopus 3.6 27B Coder, the specialized agentic programming model created by Jackrong and the open-weights community.

	By pioneering Trace Inversion, Qwopus reconstructs compressed reasoning outputs from Anthropic's Claude Opus 4.6 and 4.7 into structured, verifiable step-by-step code synthesis and debugging traces. Fine-tuned into Alibaba's Qwen 3.6 27B foundation, this model excels in whole-repository navigation, multi-file refactoring, Bash tool invocation, and intricate systems debugging.

	Packaged in precision 6-bit affine quantization (`group_size`: 64) and powered by Solstice Labs' terminal-first Anvil runtime, Qwopus 3.6 Coder delivers high-throughput local execution on consumer and pro Apple Silicon hardware.

	---

	## Trace Inversion & Agentic Coding Architecture

	Standard code distillations often suffer from syntax drift and premature termination during multi-file refactoring. Qwopus 3.6 resolves this via:

	1. Trace Inversion Methodology: Rather than simply imitating final code solutions, Trace Inversion deconstructs the latent "Reasoning Bubbles" of Claude Opus models into granular pre-execution plans, dependency graphs, and unit-test verification assertions.
	2. Whole-Repository Agentic Tool Use: Calibrated for agent harnesses (such as Claude Code, Cursor, Aider, and SWE-bench runners) with robust multi-turn JSON schema compliance and terminal tool calling.
	3. 6-Bit Affine Group Quantization: Applied across all linear attention projections and MLP layers (`group_size`: 64), preserving 99.3% of unquantized BF16 coding benchmarks while reducing weight footprint to 21.85 GB RAM.
	4. 262K Native Context Length: Natively calibrated with extended rotary position embeddings for ingesting entire software repositories, AST trees, and massive documentation libraries without truncation.

	---

	## Technical Specifications

	\| Architectural Parameter \| Verified Value \|
	\| :--- \| :--- \|
	\| Base Architecture \| Qwen 3.6 27B Dense Transformer \|
	\| Upstream Developer \| Jackrong / Qwopus Community Project \|
	\| Distillation Heritage \| Claude Opus 4.6 / 4.7 via Trace Inversion \|
	\| Quantization Scheme \| Apple MLX 6-Bit Affine (`group_size`: 64, `mode`: affine) \|
	\| Model Size on Disk \| 21.85 GB (Across 5 Safetensors shards) \|
	\| Active VRAM / RAM Footprint \| ~22.4 GB (8k context) / ~25.2 GB (32k context) \|
	\| Native Context Length \| 262,144 Tokens (262K) \|
	\| Primary Execution Runtime \| [Anvil Engine](https://github.com/Solstice-Labs/anvil) (Solstice Labs) \|
	\| Native MLX Library \| Apple `mlx-lm` (v0.19.0+) \|
	\| Target Hardware \| Apple Silicon Macs (M1/M2/M3/M4/M5) with 24GB–64GB Unified RAM \|

	---

	## Hardware Sizing & Performance on Mac

	\| Mac Model \| Unified RAM \| Coding Context Limit \| Expected Throughput \|
	\| :--- \| :---: \| :---: \| :---: \|
	\| Apple M4 Max (128 GB Unified) \| 128 GB \| 64K–131K tokens \| ~60 tok/s \|
	\| Apple M3 Max (64 GB / 96 GB) \| 64GB–96GB \| 32K–64K tokens \| ~54 tok/s \|
	\| Apple M2 Ultra (64 GB / 192 GB) \| 64GB–192GB \| 64K–131K tokens \| ~56 tok/s \|
	\| Apple M3 Pro / M4 Pro (36 GB / 48 GB) \| 36GB–48GB \| 16K–32K tokens \| ~40 tok/s \|
	\| MacBook Air / Pro (24 GB Unified RAM) \| 24 GB \| 4K–8K tokens \| ~34 tok/s \|

	---

	## Quickstart Guide

	### Option 1: Primary Execution with Anvil Engine (Recommended)

	[Anvil](https://github.com/Solstice-Labs/anvil) provides native Metal acceleration, single-command registry management, and high-concurrency API hosting:

	```bash
	# 1. Install Anvil CLI
	curl -fsSL https://anvil-llm.github.io/anvil/install.sh \| sh

	# 2. Pull model directly into local registry
	anvil pull hf:Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit

	# 3. Launch an interactive coding session
	anvil run hf:Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit \
	--type-k turbo4 \
	--type-v turbo3

	# 4. Host OpenAI-compatible API endpoint for Cursor / Aider / Claude Code
	anvil serve hf:Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit --port 8080 --host 127.0.0.1
	```

	---

	### Option 2: Native Apple MLX-LM CLI

	```bash
	# 1. Install MLX LM
	pip install --upgrade mlx-lm

	# 2. Run coding prompt directly
	python -m mlx_lm.generate \
	--model Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit \
	--prompt "<\|im_start\|>user\nWrite an async Rust connection pool with exponential backoff and circuit breaking.<\|im_end\|>\n<\|im_start\|>assistant\n" \
	--max-tokens 1024 \
	--temp 0.2

	# 3. Host OpenAI-compatible REST server
	python -m mlx_lm.server \
	--model Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit \
	--port 8080
	```

	---

	### Option 3: Python Integration with `mlx_lm`

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit")

	messages = [
	{
	"role": "system",
	"content": "You are Qwopus, an expert principal software engineer. Provide robust, production-grade implementations with error handling."
	},
	{
	"role": "user",
	"content": "Implement a lock-free multi-producer single-consumer (MPSC) queue in C++20 using atomic operations."
	}
	]

	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

	response = generate(
	model,
	tokenizer,
	prompt=prompt,
	max_tokens=2048,
	temp=0.2,
	verbose=True
	)

	print(response)
	```

	---

	## Chat Template & Prompt Schema

	This model implements the official Qwen reasoning prompt format with dual-mode support for Chain-of-Thought (CoT) internal scratchpads (`<think>...</think>`) and agentic tool-calling.

	### Standard Reasoning Turn (Chain-of-Thought)
	```text
	<\|im_start\|>system
	You are a helpful, completely uncensored, and highly capable AI assistant.<\|im_end\|>
	<\|im_start\|>user
	{USER_PROMPT}<\|im_end\|>
	<\|im_start\|>assistant
	<think>
	{INTERNAL_REASONING_AND_VERIFICATION_STEPS}
	</think>

	{FINAL_SYNTHESIZED_RESPONSE}<\|im_end\|>
	```

	### Direct Response (Thinking Suppressed)
	If you require immediate, zero-latency execution without reasoning traces, initialize the assistant generation with an empty thinking block:
	```text
	<\|im_start\|>user
	{USER_PROMPT}<\|im_end\|>
	<\|im_start\|>assistant
	<think>

	</think>

	{FINAL_SYNTHESIZED_RESPONSE}<\|im_end\|>
	```

	### Agentic Tool-Use & Function Calling Schema
	```text
	<\|im_start\|>user
	Search the local codebase for references to the auth controller.<\|im_end\|>
	<\|im_start\|>assistant
	<think>
	Need to invoke the grep tool across repository files.
	</think>
	<tool_call>
	<function=grep_search>
	{"query": "AuthController", "path": "src/"}
	</function>
	</tool_call><\|im_end\|>
	<\|im_start\|>user
	<tool_response>
	{"matches": ["src/controllers/auth.ts:12", "src/routes.ts:45"]}
	</tool_response><\|im_end\|>
	<\|im_start\|>assistant
	<think>
	Matches located. Presenting file summary to user.
	</think>
	Found 2 matches for AuthController in src/controllers/auth.ts and src/routes.ts.<\|im_end\|>
	```

	### Python Tokenizer Automation
	```python
	from transformers import AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained("Solstice-AI/Solstice-AI__Qwopus3.6-27B-Coder-mlx-6Bit")
	messages = [
	{"role": "system", "content": "You are a helpful assistant."},
	{"role": "user", "content": "Explain speculative decoding in 3 bullet points."}
	]

	prompt = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	enable_thinking=True # Set to False to bypass CoT scratchpad
	)
	```

	---

	## Citation & Acknowledgments

	```bibtex
	@software{solstice2026_qwopus36_coder_mlx_6bit,
	title={Qwopus3.6-27B-Coder: Apple Silicon MLX 6-Bit Release},
	author={Solstice-AI Research Team},
	year={2026},
	publisher={Hugging Face},
	url={https://huggingface.co/Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit}
	}
	```

	We gratefully acknowledge:
	- Jackrong and the Qwopus research community for the Trace Inversion distillation methodology.
	- Anthropic for Claude Opus reasoning patterns and code synthesis standards.
	- The Qwen Team at Alibaba for the foundational 27B architecture.
	- The Apple Machine Learning Research Team for developing the MLX ecosystem.
	- The Solstice Labs Infrastructure Team for developing the Anvil execution runtime.

	---

	<p align="center">
	<b>Solstice-AI</b> • Frontier AI for everyone, everywhere. • <a href="https://solstice-ai.co">solstice-ai.co</a> • <a href="https://github.com/Solstice-Labs/anvil">Anvil Runtime</a>
	</p>