Instructions to use Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced 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("Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced")

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 Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced"

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": "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced 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 "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced"

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 Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced"

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 "Shiftedx/Qwopus3.8-27B-Coder-Flash-MLX-Balanced" \
  --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-Coder-Flash-MLX-Balanced / evaluation_summary.json

Shiftedx

Publish mixed-precision Coder Flash with integrity manifest and measured capability limits

b9ba72c verified 3 days ago

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1.12 kB

{
  "suite": "local executable coding smoke, eight tasks",
  "coding_passes": 6,
  "coding_total": 8,
  "host": "Apple M4 Max, 40 GPU cores, 64 GiB unified memory",
  "runtime": "Kapitsa 0.2.27",
  "decoding": "DFlash2/KFlash",
  "slots": 1,
  "disk_cache": false,
  "sampling": "greedy; temperature=0, top_p=1, top_k=0; enable_thinking=false",
  "context_tests": [
    {
      "context_limit": 65536,
      "prompt_tokens": 61441,
      "retrieval": true,
      "tool_call": true,
      "tool_result": true,
      "cached_coding": true,
      "cold_seconds": 192.87,
      "cached_code_tps": 78.4
    },
    {
      "context_limit": 131072,
      "prompt_tokens": 126976,
      "retrieval": true,
      "tool_call": true,
      "tool_result": true,
      "cached_coding": true,
      "cold_seconds": 579.09,
      "cached_code_tps": 61.9
    }
  ],
  "measurement_note": "Single long-context run per configuration. Fast context results use a prior reproducible allocation of the same edited BF16 parent. Both coding graders accept standard-library typing imports. Exploratory local fixtures; BF16 parity untested."
}

	{
	"suite": "local executable coding smoke, eight tasks",
	"coding_passes": 6,
	"coding_total": 8,
	"host": "Apple M4 Max, 40 GPU cores, 64 GiB unified memory",
	"runtime": "Kapitsa 0.2.27",
	"decoding": "DFlash2/KFlash",
	"slots": 1,
	"disk_cache": false,
	"sampling": "greedy; temperature=0, top_p=1, top_k=0; enable_thinking=false",
	"context_tests": [
	{
	"context_limit": 65536,
	"prompt_tokens": 61441,
	"retrieval": true,
	"tool_call": true,
	"tool_result": true,
	"cached_coding": true,
	"cold_seconds": 192.87,
	"cached_code_tps": 78.4
	},
	{
	"context_limit": 131072,
	"prompt_tokens": 126976,
	"retrieval": true,
	"tool_call": true,
	"tool_result": true,
	"cached_coding": true,
	"cold_seconds": 579.09,
	"cached_code_tps": 61.9
	}
	],
	"measurement_note": "Single long-context run per configuration. Fast context results use a prior reproducible allocation of the same edited BF16 parent. Both coding graders accept standard-library typing imports. Exploratory local fixtures; BF16 parity untested."
	}