Instructions to use RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4 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("RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4") config = load_config("RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4") # 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
- Pi
How to use RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4"
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": "RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4 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 "RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4"
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 RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4"
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 "RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4" \ --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"
Qwen3.8-Flash-Next calibrated GPTQ MXFP4 research archive
This is a campaign-owned MLX research conversion of
Qwen/Qwen3.8-Flash-Next.
The resident transformer backbone is native MXFP4 with group size 32. The
n-gram PLE values, embedding, and language-model head remain BF16.
The archive preserves the packed backbone and BF16 PLE source planes together. It is intended for reproducibility and continued MLX runtime research; it is not a drop-in stock MLX-LM checkpoint.
Quantization and coverage
| Property | Value |
|---|---|
| Source revision | de4b8e4d43b917e7706784d8bb445c9af86a3540 |
| Backbone weight format | MXFP4 E2M1 |
| Scale format | E8M0 |
| Group size | 32 |
| Stage A tensor records | 74,400 |
| Layers completed | 48/48 |
| Packed tensors | 2,160 |
| Safetensors shards | 34 |
| Packed model bytes | 171,213,918,824 |
| PLE, embedding, and head | BF16 |
The GPTQ calibration used the same private pinned activation corpus and sequential native-scale solver as the Qwen3.8-27B MXFP4 release. Full PLE planes were not materialized in process during packing.
Runtime boundary
Stock MLX-LM does not load this archive directly because the large PLE planes remain in source shards and require a PLE-aware selected-row reader. The research runtime parses safetensors byte ranges, fetches only requested rows, deduplicates requests, coalesces contiguous ranges, and keeps a bounded row cache.
The loader-side research artifact contains 2,032 backbone keys and 68,813,407,880 model bytes after excluding the PLE-only planes. It is not standalone either: it must be paired with the PLE shards in this archive or an exact copy of their rows.
Text-only MRoPE parity was checked against the installed Transformers
reference. The maximum cosine and sine differences were approximately
0.00195 and 0.00189, attributable to BF16 rounding. This does not validate
vision inputs.
Verification and limitations
- All Stage A tensor records were consumed.
- All 34 packed shards were readable and SHA-256 checked.
- The safetensors index maps every packed tensor exactly.
- Two real fixed-prompt Apple Silicon runs generated 32/32 tokens with byte-identical text.
- Those runs used 46.80 GB and 47.25 GB peak process RSS.
- PLE gather time was 29.03 ms and 27.29 ms cumulative over 32 generated tokens; storage access was not the measured bottleneck.
These checks prove bounded loading and deterministic text on one prompt. They do not establish perplexity, general generation quality, multimodal parity, or production throughput. The custom PLE-aware MLX-LM reader is not included in this model repository.
- Downloads last month
- 350
Quantized
Model tree for RESMP-DEV/Qwen3.8-Flash-Next-GPTQ-MXFP4
Base model
Qwen/Qwen3.8-Flash-Next