Alibaba's Qwen3-4B-Instruct-2507 Jumps 45 Points on Logic, Fits in 3GB
Unsloth released dynamic GGUF quants of Qwen3-4B-Instruct-2507 , now at 838K downloads. MMLU-Pro climbs to 69.6, beating GPT-4.1-nano's 62.8 at 4B parameters. AIME25 math score jumps from 19.1 to 47.4 versus the prior Qwen3-4B.
Takeaways
- Unsloth released dynamic GGUF quants of Qwen3-4B-Instruct-2507 , now at 838K downloads.
- MMLU-Pro climbs to 69.6, beating GPT-4.1-nano's 62.8 at 4B parameters.
- AIME25 math score jumps from 19.1 to 47.4 versus the prior Qwen3-4B.
- Native 256K context window for long-document RAG and summarization workloads.
- UD-Q4_K_XL quant runs on 8GB RAM via llama.cpp, Ollama, or Docker Model Runner.
- Weak on agentic benchmarks: TerminalBench Hard 4.5%, TAU2 26.6%, Aider-Polyglot 12.9.
Alibaba’s Qwen team released Qwen3-4B-Instruct-2507 in July 2025, and Unsloth converted the checkpoint into GGUF files for llama.cpp, Ollama, and Docker Model Runner. Unsloth’s GGUF repository displayed more than 838,000 downloads at the time of writing. Selected quantizations occupy roughly 2 to 3 GB, giving developers a practical local model with stronger reasoning, coding, and instruction-following scores than the previous 4B release.
| Property | Value |
|---|---|
| Parameters | 4 billion |
| Architecture | Dense, decoder-only transformer |
| Response mode | Instruct, non-thinking |
| Maximum context | 262,144 tokens |
| License | Apache 2.0 |
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