Instructions to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality")
config = load_config("Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality"

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": "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality 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 "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality"

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 Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality"

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 "Youssofal/Qwen3.8-Flash-Next-MTPLX-Optimized-Quality" \
  --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"

Youssofal commited on 9 days ago

Commit

3908bbf

verified ·

1 Parent(s): 072dd0b

Describe the streaming audit in a valid explicitly unverified runtime contract

Browse files

Files changed (2) hide show

mtplx_runtime.json +11 -1
size-checksums.json +3 -3

mtplx_runtime.json CHANGED Viewed

@@ -2260,5 +2260,15 @@
     "full_load_verified": false,
     "mode": "streaming",
     "status": "streaming-audited"
-  }
 }

     "full_load_verified": false,
     "mode": "streaming",
     "status": "streaming-audited"
+  },
+  "arch_id": "qwen4-next",
+  "model_family": "qwen4_exp",
+  "served_model_id": "mtplx-flash-next-optimized-quality",
+  "mtp_depth_max": 3,
+  "recommended_profile": "turbo",
+  "exactness_baseline": {
+    "status": "pending_full_load",
+    "streaming_audit": "streaming_audit.json"
+  },
+  "verified_on": {}
 }

size-checksums.json CHANGED Viewed

@@ -189,8 +189,8 @@
       "sha256": "12afaa2e2b1bf263632ffcc5c22c7822c3b4ebc138fe60e1959682e9418f9da3"
     },
     "mtplx_runtime.json": {
-      "bytes": 144496,
-      "sha256": "2baa63c311f689e661e99be1014bd1364fd94d8732331dde564c1344014c08c1"
     },
     "ngram-table.safetensors": {
       "bytes": 32000153976,
@@ -225,6 +225,6 @@
       "sha256": "ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003"
     }
   },
-  "total_bytes_excluding_manifest": 169958526656,
   "weight_hash_source": "Successful streaming audit from the completed build; immutable weight files reused."
 }

       "sha256": "12afaa2e2b1bf263632ffcc5c22c7822c3b4ebc138fe60e1959682e9418f9da3"
     },
     "mtplx_runtime.json": {
+      "bytes": 144802,
+      "sha256": "5b9c6c7c5086be506ebb9d7258fcd959965c71b8c7ce5c8d5e9501bcb05b64d2"
     },
     "ngram-table.safetensors": {
       "bytes": 32000153976,
       "sha256": "ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003"
     }
   },
+  "total_bytes_excluding_manifest": 169958526962,
   "weight_hash_source": "Successful streaming audit from the completed build; immutable weight files reused."
 }