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 / structured_output_validation.json

Shiftedx

Document tested strict JSON profiles, vision results and repeated speed comparison

795b0d8 verified 4 days ago

Raw History Blame Contribute Delete

1.68 kB

{
  "runtime": "Kapitsa 0.2.31 (84249c0)",
  "host": "Apple M4 Max, 40-core GPU, 64 GiB",
  "context_limit": 131072,
  "sampler": "greedy; thinking disabled",
  "implementation": "request-level strict JSON Schema via response_format; no weight or tokenizer changes; no response postprocessing",
  "schema_inputs": "Field types and structural constraints only; expected answers are excluded from requests.",
  "test_source_sha256": "b0e5013aecfe29e2b966afed82120926b700ffd8860a8129ee08e5cdecb62088",
  "schema_file": "structured_output_profiles.json",
  "fixture_seeds": {
    "original": [
      7319,
      173
    ],
    "held_out": [
      27183,
      65537,
      42017
    ]
  },
  "vision": [
    {
      "mode": "dflash2",
      "lane": "baseline",
      "passed": 4,
      "total": 8
    },
    {
      "mode": "dflash2",
      "lane": "schema-r1",
      "passed": 8,
      "total": 8
    },
    {
      "mode": "dflash2",
      "lane": "schema-r2",
      "passed": 8,
      "total": 8
    },
    {
      "mode": "dflash2",
      "lane": "holdout",
      "passed": 12,
      "total": 12
    },
    {
      "mode": "mtp3",
      "lane": "schema-mtp",
      "passed": 8,
      "total": 8
    }
  ],
  "coding_passed": 6,
  "coding_total": 8,
  "coding_grader_note": "The same standard-library typing allowance is applied to both models.",
  "speed": {
    "text_median_decode_tps": 90.2,
    "structured_json_median_decode_tps": 128.1,
    "text_probes": 2,
    "repeats": 3,
    "measurement": "Median server-reported decode; code/prose capped at 256 tokens; JSON sequence independently checked for exact correctness. Six text and three JSON measurements per model."
  }
}

	{
	"runtime": "Kapitsa 0.2.31 (84249c0)",
	"host": "Apple M4 Max, 40-core GPU, 64 GiB",
	"context_limit": 131072,
	"sampler": "greedy; thinking disabled",
	"implementation": "request-level strict JSON Schema via response_format; no weight or tokenizer changes; no response postprocessing",
	"schema_inputs": "Field types and structural constraints only; expected answers are excluded from requests.",
	"test_source_sha256": "b0e5013aecfe29e2b966afed82120926b700ffd8860a8129ee08e5cdecb62088",
	"schema_file": "structured_output_profiles.json",
	"fixture_seeds": {
	"original": [
	7319,
	173
	],
	"held_out": [
	27183,
	65537,
	42017
	]
	},
	"vision": [
	{
	"mode": "dflash2",
	"lane": "baseline",
	"passed": 4,
	"total": 8
	},
	{
	"mode": "dflash2",
	"lane": "schema-r1",
	"passed": 8,
	"total": 8
	},
	{
	"mode": "dflash2",
	"lane": "schema-r2",
	"passed": 8,
	"total": 8
	},
	{
	"mode": "dflash2",
	"lane": "holdout",
	"passed": 12,
	"total": 12
	},
	{
	"mode": "mtp3",
	"lane": "schema-mtp",
	"passed": 8,
	"total": 8
	}
	],
	"coding_passed": 6,
	"coding_total": 8,
	"coding_grader_note": "The same standard-library typing allowance is applied to both models.",
	"speed": {
	"text_median_decode_tps": 90.2,
	"structured_json_median_decode_tps": 128.1,
	"text_probes": 2,
	"repeats": 3,
	"measurement": "Median server-reported decode; code/prose capped at 256 tokens; JSON sequence independently checked for exact correctness. Six text and three JSON measurements per model."
	}
	}