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BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF overview

BoneMangler/Qwen3 Coder 42B A3B Instruct TOTAL RECALL MASTER CODER M 512k ctx W4A16 Q8 0 GGUF This model was converted to GGUF format from tcclaviger/Qwen3 Cod…

transformersggufquantizedgptqw4a16llm-compressorqwen3mixture-of-expertscodingprogrammingcode generationcodecodeqwenmoecoderqwen2chatqwenqwen-coderQwen3-30B-A3Bmixture of experts128 experts8 active experts512k context

Runs locally from ~41.98 GB disk (32 GB+ VRAM class GPUs with llama.cpp / guIDE).

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Pipeline
text-generation

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1 GGUF files detected
Direct downloads for local inference
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qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.ggufGGUFQ8_041.98 GBDownload

Model Details

Model IDBoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF
AuthorBoneMangler
Pipelinetext-generation
Licenseapache-2.0
Base modeltcclaviger/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16
Last modified2026-06-17T06:40:53.000Z

Model README

---

license: apache-2.0

library_name: transformers

model_size: 42B

language:

  • en
  • fr
  • zh
  • de

tags:

  • quantized
  • gptq
  • w4a16
  • llm-compressor
  • qwen3
  • mixture-of-experts
  • coding
  • programming
  • code generation
  • code
  • codeqwen
  • moe
  • coder
  • qwen2
  • chat
  • qwen
  • qwen-coder
  • Qwen3-30B-A3B
  • mixture of experts
  • 128 experts
  • 8 active experts
  • 512k context
  • finetune
  • brainstorm 20x
  • brainstorm
  • optional thinking
  • qwen3_moe
  • rocm
  • amd
  • r9700
  • RDNA4
  • gfx1201
  • ultra quality
  • llama-cpp
  • gguf-my-repo

base_model: tcclaviger/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16

pipeline_tag: text-generation

---

BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF

This model was converted to GGUF format from tcclaviger/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16 using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF --hf-file qwen3-coder-42b-a3b-instruct-total-recall-master-coder-m-512k-ctx-w4a16-q8_0.gguf -c 2048

Run BoneMangler/Qwen3-Coder-42B-A3B-Instruct-TOTAL-RECALL-MASTER-CODER-M-512k-ctx-W4A16-Q8_0-GGUF with guIDE

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