--- title: "Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/238d4a60-b5ce-4e02-a828-5e9dce37a102" updated: "2026-11-13" --- # Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. An English-only coding model in GGUF, a 42.6B mixture-of-experts quant of about 21.8 GB, for code completion, refactoring and agentic tool calls in llama.cpp or LM Studio. Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by IsValorum ## About ### What it is This is a GGUF quantization of an experimental coding model, made by IsValorum from Jab1718's qwen3.8-flash-coder-85gb-bf16. That upstream model is an experimental pruned slice, with 352 of its 512 routed experts removed using English Python and SWE-bench data. The result is a coding specialist for English only. The file is about 21.77 GB at 3.45 bits per weight, against 85.30 GB for the uncompressed BF16 version. ### Who it suits Developers who want a local English coding model that fits in a 21 to 22 GB file, including people with a 24GB workstation. It also suits anyone comparing quantization recipes for mixture-of-experts models. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Complete and refactor code in English | Ask it to tidy a Python function | | Agent building | Agent developers | Run a local model for agentic tool calls | Point a coding agent at a llama-server instance | | Research | Quantization researchers | Compare a tensor-by-tensor quant with the BF16 baseline | Read the perplexity table for the three releases | | Hardware enthusiasts | Workstation owners | Fit a 42.6B model in 21 to 22 GB | Try it with some layers offloaded to system RAM | People who need other languages or general chat can skip it, since the card says output breaks down outside English coding. ### Where to use it - Stock llama.cpp, through llama-server - LM Studio - Windows, macOS or Linux with Python, for downloading the files The card says it does not work with Strata Engine, because of its 160-expert layout. | Use case | Who | What they get back | | --- | --- | --- | | Code completion in English | Developers | Suggested code for Python and similar tasks | | Refactoring | Developers | Cleaner versions of code you paste in | | Agentic tool calling | Agent builders | Tool calls from a local coding model | ### How to set it up Make room for a file of about 21.77 GB, plus memory for the model to run. - Python and pip, for the Hugging Face download tools - Stock llama.cpp with llama-server, or LM Studio - About 21 to 22 GB of free disk and enough RAM or VRAM to load it 1. Install the huggingface\_hub package. 2. Download the files with the hf command. 3. Open the GGUF file in LM Studio, or serve it with llama-server. 4. Read the card's recommended settings before you start. ```bash pip install -U huggingface_hub hf download IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF ``` ### How to use it Give it coding tasks in English, such as completing, refactoring or explaining code. The card's contents list names recommended generation parameters, a repeat penalty advisory for coding and a chat template for agentic use with a reasoning effort setting. Those sections are cut off here, so read them on the model page. | Tool | What it does | | --- | --- | | llama-server | Serves the GGUF file from stock llama.cpp | | LM Studio | Loads the GGUF file in a desktop app | | hf download | Fetches the files from Hugging Face | ### Example You load the file in LM Studio and ask for "Refactor this Python function so it no longer uses nested loops." The model answers with code. According to the card, the same request in Spanish or German would come back as broken text. ### For newcomers and experts Think of this as a coding assistant squeezed into one big file. It only works well in English and only for programming. A good first try is to download it, load it in LM Studio and ask for a small Python change. Underneath, the upstream model has 48 hybrid layers, 36 linear SSM DeltaNet and 12 sparse full attention, with 160 experts left after pruning. The quantization assigns precision tensor by tensor to hold reasoning quality and routing. The trade-off is a pruned model with no recovery fine-tune yet. Start from Qwen3.8 Flash Coder → Prune 352 of 512 routed experts → Quantize tensor by tensor to GGUF → Run in llama-server or LM Studio ### Good to know The card compares three releases on WikiText-2 perplexity, where lower is better. The MiniPlus file sits within 0.17% of the BF16 reference. File size in GB | Label | Value | | --- | --- | | BF16 reference | 85.30 GB | | APEX-I-MiniPlus V2.1 | 21.77 GB | | APEX-I-NanoPlus | 18.34 GB | WikiText-2 perplexity, lower is better | Label | Value | | --- | --- | | BF16 reference | 30.0975 | | APEX-I-MiniPlus V2.1 | 30.1495 | | APEX-I-NanoPlus | 34.4199 | - Warning: the card marks this an experimental pre-release, and its pruned experts hurt general chat and other languages - Size: 42.6B parameters in GGUF format, English listed as the language - License: Apache 2.0 - Cost to run: no API key or paid service is named, though the author asks for optional donations - Data it reaches: the files you download from Hugging Face and the prompts you give your local runner - Not for: languages other than English, general chit-chat, and Strata Engine ## README ````markdown # Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF An English-only coding model in GGUF, a 42.6B mixture-of-experts quant of about 21.8 GB, for code completion, refactoring and agentic tool calls in llama.cpp or LM Studio. Made by IsValorum on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a GGUF quantization of an experimental coding model, made by IsValorum from Jab1718's qwen3.8-flash-coder-85gb-bf16. That upstream model is an experimental pruned slice, with 352 of its 512 routed experts removed using English Python and SWE-bench data. The result is a coding specialist for English only. The file is about 21.77 GB at 3.45 bits per weight, against 85.30 GB for the uncompressed BF16 version. Who it suits: Developers who want a local English coding model that fits in a 21 to 22 GB file, including people with a 24GB workstation. It also suits anyone comparing quantization recipes for mixture-of-experts models. ```table Industry | Who uses it | How they use it | Example Software | Developers | Complete and refactor code in English | Ask it to tidy a Python function Agent building | Agent developers | Run a local model for agentic tool calls | Point a coding agent at a llama-server instance Research | Quantization researchers | Compare a tensor-by-tensor quant with the BF16 baseline | Read the perplexity table for the three releases Hardware enthusiasts | Workstation owners | Fit a 42.6B model in 21 to 22 GB | Try it with some layers offloaded to system RAM ``` People who need other languages or general chat can skip it, since the card says output breaks down outside English coding. Where to use it: - Stock llama.cpp, through llama-server - LM Studio - Windows, macOS or Linux with Python, for downloading the files The card says it does not work with Strata Engine, because of its 160-expert layout. ```table Use case | Who | What they get back Code completion in English | Developers | Suggested code for Python and similar tasks Refactoring | Developers | Cleaner versions of code you paste in Agentic tool calling | Agent builders | Tool calls from a local coding model ``` How to set it up: Make room for a file of about 21.77 GB, plus memory for the model to run. - Python and pip, for the Hugging Face download tools - Stock llama.cpp with llama-server, or LM Studio - About 21 to 22 GB of free disk and enough RAM or VRAM to load it 1. Install the huggingface_hub package. 2. Download the files with the hf command. 3. Open the GGUF file in LM Studio, or serve it with llama-server. 4. Read the card's recommended settings before you start. ```bash pip install -U huggingface_hub hf download IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF ``` How to use it: Give it coding tasks in English, such as completing, refactoring or explaining code. The card's contents list names recommended generation parameters, a repeat penalty advisory for coding and a chat template for agentic use with a reasoning effort setting. Those sections are cut off here, so read them on the model page. ```table Tool | What it does llama-server | Serves the GGUF file from stock llama.cpp LM Studio | Loads the GGUF file in a desktop app hf download | Fetches the files from Hugging Face ``` Example: You load the file in LM Studio and ask for "Refactor this Python function so it no longer uses nested loops." The model answers with code. According to the card, the same request in Spanish or German would come back as broken text. For newcomers and experts: Think of this as a coding assistant squeezed into one big file. It only works well in English and only for programming. A good first try is to download it, load it in LM Studio and ask for a small Python change. Underneath, the upstream model has 48 hybrid layers, 36 linear SSM DeltaNet and 12 sparse full attention, with 160 experts left after pruning. The quantization assigns precision tensor by tensor to hold reasoning quality and routing. The trade-off is a pruned model with no recovery fine-tune yet. ```flow Start from Qwen3.8 Flash Coder Prune 352 of 512 routed experts Quantize tensor by tensor to GGUF Run in llama-server or LM Studio ``` Good to know: The card compares three releases on WikiText-2 perplexity, where lower is better. The MiniPlus file sits within 0.17% of the BF16 reference. ```chart Title: File size in GB BF16 reference: 85.30 GB APEX-I-MiniPlus V2.1: 21.77 GB APEX-I-NanoPlus: 18.34 GB ``` ```chart Title: WikiText-2 perplexity, lower is better BF16 reference: 30.0975 APEX-I-MiniPlus V2.1: 30.1495 APEX-I-NanoPlus: 34.4199 ``` - Warning: the card marks this an experimental pre-release, and its pruned experts hurt general chat and other languages - Size: 42.6B parameters in GGUF format, English listed as the language - License: Apache 2.0 - Cost to run: no API key or paid service is named, though the author asks for optional donations - Data it reaches: the files you download from Hugging Face and the prompts you give your local runner - Not for: languages other than English, general chit-chat, and Strata Engine ## On Hugging Face, 13 November 2026 - Library: gguf - Parameters: 42.6B - Weights: about 21.3 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 1,585 - Likes: 8 - License: apache-2.0 - Languages: en - Based on: Jab1718/qwen3.8-flash-coder-85gb-bf16 - Tags: gguf, qwen3.8, llama.cpp, quantized, quantization, apex, apex-quant, apex-i-miniplus, v2.1, qwen, qwen4, qwen4-exp ## Links - Model: https://huggingface.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF - Files: https://huggingface.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF on Hugging Face](https://huggingface.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Qwen3.8-Flash-Coder APEX-I-MiniPlus V2.1 GGUF (this page) | 1,585 | 8 | 42.6B | apache-2.0 | 2026-10-11 | | [Axion-1.0-35B-APEX-I-MiniPlus-V2.1-GGUF](https://mindket.com/p/c72e0389-3329-43ad-bfb2-f48f037bce1f) | 686 | 4 | 34.7B | apache-2.0 | 2026-11-05 | | [Qwen3.6-35B-A3B 2-bit GSQ GGUF](https://mindket.com/p/6d3048f5-cd06-4079-a604-ac9311e5fb97) | 9,309 | 18 | 34.7B | apache-2.0 | 2026-10-22 | | [Qwen3.8-27B iMatrix NVFP4 MTP GGUF](https://mindket.com/p/d44ebd5f-22b5-4277-81d0-197775776c4f) | 3,271,044 | 51 | 461M | apache-2.0 | 2026-08-17 | | [Qwen3.6-35B-A3B lododo-Distill-v1.0 (GGUF)](https://mindket.com/p/5b90e607-2897-48bb-9953-ac8e99d39909) | 746 | 8 | 35.5B | apache-2.0 | 2026-10-23 | | [Qwen3.8-27B Intel Arc Tuned](https://mindket.com/p/83aecdc8-7c11-4fdd-a57d-ac9e89b7742c) | 7,481 | 31 | 26.9B | apache-2.0 | 2026-10-09 | Numbers as Mindket last read them, on 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-11 - First published: 2026-10-05 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF on Hugging Face](https://huggingface.co/IsValorum/Qwen3.8-Flash-Coder-APEX-I-MiniPlus-V2.1-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/238d4a60-b5ce-4e02-a828-5e9dce37a102) - [Its page on Mindket](https://mindket.com/p/238d4a60-b5ce-4e02-a828-5e9dce37a102)