Instructions to use shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit")
model = AutoModelForMultimodalLM.from_pretrained("shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use shreyan35/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("shreyan35/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

vLLM

How to use shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit

SGLang

How to use shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit",
    max_seq_length=2048,
)

How to use shreyan35/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 "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit"

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": "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use shreyan35/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 "shreyan35/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 "shreyan35/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"

MLX LM

How to use shreyan35/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 "shreyan35/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 "shreyan35/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": "shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit with Docker Model Runner:
```
docker model run hf.co/shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit
```

Hermes Agent

How to use shreyan35/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 "shreyan35/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 shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit

Run Hermes

hermes

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

shreyan35

Update README.md

8f7428e verified about 1 month ago

preview code

Raw

History Blame Contribute Delete

3.07 kB

	---
	base_model: Jackrong/Qwopus3.6-27B-Coder
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen3_6
	- reasoning
	- chain-of-thought
	- lora
	- sft
	- agent
	- tool-use
	- function-calling
	- coder
	- mlx
	license: apache-2.0
	language:
	- en
	- zh
	- es
	- ru
	- ja
	pipeline_tag: text-generation
	datasets:
	- Jackrong/Claude-opus-4.6-TraceInversion-9000x
	- Jackrong/Claude-opus-4.7-TraceInversion-5000x
	- lambda/hermes-agent-reasoning-traces
	---

	# Qwopus 3.6 27B Coder (6-bit MLX Quantization)

	This repository hosts a high-performance 6-bit MLX quantization of `Jackrong/Qwopus3.6-27B-Coder`, converted using `mlx-lm` (v0.31.2).

	Optimized specifically for Apple Silicon (M-series chips), this model balances the exceptional coding intelligence of the 27B parameter Qwopus architecture with the memory and speed efficiency required for local, low-latency deployment.

	## Key Features

	* TurboQuant Accelerated: Leverages advanced MLX kernel optimizations for accelerated matrix multiplication, ensuring faster token generation rates during local execution, making the kv cache at the models whopping full 262k context just around ~3-4gb from ~180gb
	* ~50% Memory Reduction: Cuts VRAM usage nearly in half compared to the base 16-bit model, opening up local execution on mid-tier unified memory configurations without spilling into system swap.
	* Near-Lossless Precision: Grouped 6-bit quantization maintains the original model's structural code generation capabilities, logic, and syntax proficiency with negligible degradation.
	* Apple Silicon Native: Designed from the ground up for unified memory architectures, leveraging direct hardware acceleration via the MLX framework.

	---

	## Installation

	Ensure you have the latest version of the MLX language model library installed:
	```
	pip install mlx-lm
	```

	## Quick Start

	You can load and run inference with this model locally using the following Python script. It automatically detects and applies the correct chat template for structured instruction-following.

	```
	from mlx_lm import load, generate

	# Load the optimized 6-bit model and its tokenizer
	model, tokenizer = load("shreyan35/Qwopus3.6-27B-Coder-mlx-6Bit")

	# Define your programming task or prompt
	prompt = "Write an optimized Python function to find the longest palindromic substring."

	# Apply the model's native chat template if available
	if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
	messages = [{"role": "user", "content": prompt}]
	prompt = tokenizer.apply_chat_template(
	messages, tokenize=False, add_generation_prompt=True
	)

	# Generate the response with real-time streaming/verbosity
	response = generate(model, tokenizer, prompt=prompt, verbose=True)
	```
	# Acknowledgements

	* Base Model: Credit to `Jackrong` for the original [Qwopus3.6-27B-Coder](https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder) architecture.
	* Infrastructure: The Apple Machine Learning Research team for the ongoing development of the [MLX framework](https://github.com/ml-explore/mlx).