Qwen / text-generation Open weights updated 1 year ago

Qwen2.5-Coder-1.5B-Instruct-GGUF

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:

Params
1.5 B
Context
32,768
Downloads 30d
216 K
Likes
148
Commercial use: allowed apache-2.0 Not gated GGUF 1 languages View on Hugging Face ↗

Download history

daily snapshots · 13 days
217 K180 K
Oct 13Oct 17Oct 21Oct 25

Can you run it?

Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.

FileQuantSizeEst. VRAMVerdict on RTX 4090 · 24 GB
qwen2.5-coder-1.5b-instruct-q2_k.gguf Q2_K 0.8 GB 1.6 GB ✅ Runs comfortably
qwen2.5-coder-1.5b-instruct-q3_k_m.gguf Q3_K_M 0.9 GB 1.7 GB ✅ Runs comfortably
qwen2.5-coder-1.5b-instruct-q4_0.gguf Q4_0 1.1 GB 1.9 GB ✅ Runs comfortably
qwen2.5-coder-1.5b-instruct-q4_k_m.gguf Q4_K_M 1.1 GB 2.0 GB ✅ Runs comfortably
qwen2.5-coder-1.5b-instruct-q5_0.gguf Q5_0 1.3 GB 2.1 GB ✅ Runs comfortably
qwen2.5-coder-1.5b-instruct-q5_k_m.gguf Q5_K_M 1.3 GB 2.1 GB ✅ Runs comfortably
qwen2.5-coder-1.5b-instruct-q6_k.gguf Q6_K 1.5 GB 2.3 GB ✅ Runs comfortably
qwen2.5-coder-1.5b-instruct-q8_0.gguf Q8_0 1.9 GB 2.8 GB ✅ Runs comfortably
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Run it

copy-paste, exact tags checked against the Hub
~ · ollama · Q4_0
$ ollama run qwen2-5-coder-1-5b-instruct-gguf

# pin the quantization explicitly
$ ollama run qwen2-5-coder-1-5b-instruct-gguf-q4_0
est. VRAM 1.9 GBon RTX 4090 · 24 GBJSON API →

Specifications

Parameters
1.5 B
Context length
32,768
Licence
apache-2.0
First seen on the Hub
2024-09-18
Base model
Qwen2.5-Coder-1.5B-Instruct
Training datasets
undisclosed
Added to our catalog
2026-10-13

Family

Base model and the most-downloaded derivatives in the catalog.

Compare with any text-generation model