Qwen / text-generation Open weights updated 1 year ago

Qwen2.5-Coder-14B-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
14.0 B
Context
32,768
Downloads 30d
172 K
Likes
262
Commercial use: allowed apache-2.0 Not gated GGUF 1 languages View on Hugging Face ↗

Download history

tracking started — chart appears after 7 days of snapshots (1 recorded)
172 K downloads in the last 30 days

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-14b-instruct-q2_k.gguf Q2_K 5.8 GB 8.9 GB ✅ Runs comfortably
qwen2.5-coder-14b-instruct-q3_k_m.gguf Q3_K_M 7.3 GB 10.7 GB ✅ Runs comfortably
qwen2.5-coder-14b-instruct-q4_0.gguf Q4_0 8.5 GB 12.0 GB ✅ Runs comfortably
qwen2.5-coder-14b-instruct-q4_k_m.gguf Q4_K_M 9.0 GB 12.5 GB ✅ Runs comfortably
qwen2.5-coder-14b-instruct-q5_0.gguf Q5_0 10.3 GB 13.9 GB ✅ Runs comfortably
qwen2.5-coder-14b-instruct-q5_k_m.gguf Q5_K_M 10.5 GB 14.2 GB ✅ Runs comfortably
qwen2.5-coder-14b-instruct-q6_k.gguf Q6_K 12.1 GB 15.9 GB ✅ Runs comfortably
qwen2.5-coder-14b-instruct-fp16.gguf GGUF 29.5 GB 35.1 GB ❌ Won’t fit
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-14b-instruct-gguf

# pin the quantization explicitly
$ ollama run qwen2-5-coder-14b-instruct-gguf-q4_0
est. VRAM 12.0 GBon RTX 4090 · 24 GBJSON API →

Specifications

Parameters
14.0 B
Context length
32,768
Licence
apache-2.0
First seen on the Hub
2024-11-09
Base model
Qwen2.5-Coder-14B-Instruct
Training datasets
undisclosed
Added to our catalog
2026-10-25

Family

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

Compare with any text-generation model