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hariharanv04/qwen3-4b-instruct-medium4-bf16-v3-Q4_K_M-GGUF overview

hariharanv04/qwen3 4b instruct medium4 bf16 v3 Q4 K M GGUF This model was converted to GGUF format from hariharanv04/qwen3 4b instruct medium4 bf16 v3 https://…

ggufllama-cppgguf-my-repobase_model:hariharanv04/qwen3-4b-instruct-medium4-bf16-v3base_model:quantized:hariharanv04/qwen3-4b-instruct-medium4-bf16-v3endpoints_compatibleregion:usconversational

Runs locally from ~2.33 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
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qwen3-4b-instruct-medium4-bf16-v3-q4_k_m.ggufGGUFBF162.33 GBDownload

Model Details

Model IDhariharanv04/qwen3-4b-instruct-medium4-bf16-v3-Q4_K_M-GGUF
Authorhariharanv04
Pipeline
License
Base modelhariharanv04/qwen3-4b-instruct-medium4-bf16-v3
Last modified2026-06-18T07:25:34.000Z

Model README

---

base_model: hariharanv04/qwen3-4b-instruct-medium4-bf16-v3

tags:

  • llama-cpp
  • gguf-my-repo

---

hariharanv04/qwen3-4b-instruct-medium4-bf16-v3-Q4_K_M-GGUF

This model was converted to GGUF format from hariharanv04/qwen3-4b-instruct-medium4-bf16-v3 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 hariharanv04/qwen3-4b-instruct-medium4-bf16-v3-Q4_K_M-GGUF --hf-file qwen3-4b-instruct-medium4-bf16-v3-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo hariharanv04/qwen3-4b-instruct-medium4-bf16-v3-Q4_K_M-GGUF --hf-file qwen3-4b-instruct-medium4-bf16-v3-q4_k_m.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 hariharanv04/qwen3-4b-instruct-medium4-bf16-v3-Q4_K_M-GGUF --hf-file qwen3-4b-instruct-medium4-bf16-v3-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo hariharanv04/qwen3-4b-instruct-medium4-bf16-v3-Q4_K_M-GGUF --hf-file qwen3-4b-instruct-medium4-bf16-v3-q4_k_m.gguf -c 2048

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