Instructions to use TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP 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("TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP")
config = load_config("TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP")

# 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 TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP"

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": "TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP 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 "TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP"

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 TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP"

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 "TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP" \
  --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"

ashxhart commited on Oct 1

Commit

069090c

verified ·

1 Parent(s): a6ff6bc

Rebrand model card to TensorFold

Browse files

Files changed (1) hide show

README.md +15 -10

README.md CHANGED Viewed

@@ -22,6 +22,11 @@ tags:
   - 4-bit
 ---
 <p align="center">
   <a href="https://qwen.ai/"><img src="qwen-logo.png" width="96" height="95" alt="Qwen"></a>
@@ -32,7 +37,7 @@ tags:
   <img src="https://img.shields.io/badge/Qwen-Qwen3.8-615CED?style=for-the-badge&logo=qwen&logoColor=white" alt="Qwen Qwen3.8">
   <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=for-the-badge&logo=apple&logoColor=white" alt="Apple silicon MLX">
   <img src="https://img.shields.io/badge/Native_MTP-Included-22C55E?style=for-the-badge" alt="Native MTP included">
-  <img src="https://img.shields.io/badge/Vontra-oQ4-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="Vontra oQ4">
 </p>
@@ -60,7 +65,7 @@ This repository contains an oQ4 mixed-precision MLX conversion produced directly
 | Item | Value |
 | --- | --- |
-| Repository | `Vontra/Qwen3.8-Flash-Next-MLX-oQ4-MTP` |
 | Base model | [`Qwen/Qwen3.8-Flash-Next`](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) |
 | Format | MLX safetensors |
 | Quantisation | oQ4 mixed precision, 4-bit affine base with protected modules at 5/8-bit |
@@ -89,7 +94,7 @@ The upstream tokenizer, chat template, vision processor, generation configuratio
 ```bash
-hf download Vontra/Qwen3.8-Flash-Next-MLX-oQ4-MTP \
   --local-dir Qwen3.8-Flash-Next-MLX-oQ4-MTP
 ```
@@ -157,14 +162,14 @@ This is a community conversion, not an official Qwen release.
 The upstream model is released under the **Qwen Community License 1.0**. The required licence text is included in this repository.
-Model design, training, evaluations, and upstream documentation belong to Qwen and the original contributors. The MLX conversion and packaging are provided by [Vontra](https://huggingface.co/Vontra).
-<!-- vontra-chooser-start -->
 ## Choose for your Mac
-[64GB Macs](https://huggingface.co/collections/Vontra/mlx-models-for-64gb-macs-6a9fefda17932216ec9ab457) · [128GB Macs](https://huggingface.co/collections/Vontra/mlx-models-for-128gb-macs-6a9ff0abd31bc9abbe7922d7) · [256GB Macs](https://huggingface.co/collections/Vontra/mlx-models-for-256gb-macs-6a9ff0ef9fed7c5bdca15e9b)
 No measured memory tier is assigned here. The collections use published M3 Studio peaks with at least 25% nominal headroom; fit on other Macs is an estimate, and full context is not guaranteed. Start with short context and one request.
@@ -172,12 +177,12 @@ No measured memory tier is assigned here. The collections use published M3 Studi
 The exact tested oMLX application version is not recorded here; a library version is not an app version. The original performance tables retain their benchmark conditions and speed figures; this documentation update adds no new test results.
-An unresolved [long-generation repetition report](https://huggingface.co/Vontra/Qwen3.8-Flash-Next-MLX-oQ4/discussions/2) involves oQ4-MTP; this update does not establish a fix.
 ### Quick start and demo prompt
 ```bash
-hf download Vontra/Qwen3.8-Flash-Next-MLX-oQ4-MTP --local-dir ./models/Qwen3.8-Flash-Next-MLX-oQ4-MTP
 ```
 Add the downloaded folder to oMLX model directories, refresh the list, and follow this card's architecture and MTP compatibility requirements before loading.
@@ -190,5 +195,5 @@ Explain why the sky looks blue in three short sentences.
 This is a demo prompt to try, not a recorded successful run; a captured demonstration for this documentation update is not yet available.
-[Follow Vontra for new Apple Silicon releases and fixes.](https://huggingface.co/Vontra)
-<!-- vontra-chooser-end -->

   - 4-bit
 ---
+<p align="center">
+  <a href="https://tensorfold.dev">
+    <img src="https://huggingface.co/spaces/TensorFold/README/resolve/main/tensorfold-logo.png" alt="TensorFold" width="160">
+  </a>
+</p>
 <p align="center">
   <a href="https://qwen.ai/"><img src="qwen-logo.png" width="96" height="95" alt="Qwen"></a>
   <img src="https://img.shields.io/badge/Qwen-Qwen3.8-615CED?style=for-the-badge&logo=qwen&logoColor=white" alt="Qwen Qwen3.8">
   <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=for-the-badge&logo=apple&logoColor=white" alt="Apple silicon MLX">
   <img src="https://img.shields.io/badge/Native_MTP-Included-22C55E?style=for-the-badge" alt="Native MTP included">
+  <img src="https://img.shields.io/badge/TensorFold-oQ4-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="TensorFold oQ4">
 </p>
 | Item | Value |
 | --- | --- |
+| Repository | `TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP` |
 | Base model | [`Qwen/Qwen3.8-Flash-Next`](https://huggingface.co/Qwen/Qwen3.8-Flash-Next) |
 | Format | MLX safetensors |
 | Quantisation | oQ4 mixed precision, 4-bit affine base with protected modules at 5/8-bit |
 ```bash
+hf download TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP \
   --local-dir Qwen3.8-Flash-Next-MLX-oQ4-MTP
 ```
 The upstream model is released under the **Qwen Community License 1.0**. The required licence text is included in this repository.
+Model design, training, evaluations, and upstream documentation belong to Qwen and the original contributors. The MLX conversion and packaging are provided by [TensorFold](https://huggingface.co/TensorFold).
+<!-- TensorFold-chooser-start -->
 ## Choose for your Mac
+[64GB Macs](https://huggingface.co/collections/TensorFold/mlx-models-for-64gb-macs-6a9fefda17932216ec9ab457) · [128GB Macs](https://huggingface.co/collections/TensorFold/mlx-models-for-128gb-macs-6a9ff0abd31bc9abbe7922d7) · [256GB Macs](https://huggingface.co/collections/TensorFold/mlx-models-for-256gb-macs-6a9ff0ef9fed7c5bdca15e9b)
 No measured memory tier is assigned here. The collections use published M3 Studio peaks with at least 25% nominal headroom; fit on other Macs is an estimate, and full context is not guaranteed. Start with short context and one request.
 The exact tested oMLX application version is not recorded here; a library version is not an app version. The original performance tables retain their benchmark conditions and speed figures; this documentation update adds no new test results.
+An unresolved [long-generation repetition report](https://huggingface.co/TensorFold/Qwen3.8-Flash-Next-MLX-oQ4/discussions/2) involves oQ4-MTP; this update does not establish a fix.
 ### Quick start and demo prompt
 ```bash
+hf download TensorFold/Qwen3.8-Flash-Next-MLX-oQ4-MTP --local-dir ./models/Qwen3.8-Flash-Next-MLX-oQ4-MTP
 ```
 Add the downloaded folder to oMLX model directories, refresh the list, and follow this card's architecture and MTP compatibility requirements before loading.
 This is a demo prompt to try, not a recorded successful run; a captured demonstration for this documentation update is not yet available.
+[Follow TensorFold for new Apple Silicon releases and fixes.](https://huggingface.co/TensorFold)
+<!-- TensorFold-chooser-end -->