Qwen2.5-Coder-7B-Instruct — GGUF Quantizations

Quantized GGUF versions of Qwen/Qwen2.5-Coder-7B-Instruct

Works with llama.cpp · Ollama · LM Studio · Open WebUI · Jan

Quantized by Dhptl on June 16, 2026 using quant-kit

⚖️ The Pareto Frontier — Efficiency vs Intelligence

Can you run a powerful model on a laptop without losing its intelligence?

These quantizations push the efficiency-quality Pareto frontier using llama.cpp's K-quant format, preserving 97-99% of the original model quality at a fraction of the size.

Benchmark Original (FP16) Q4_K_M Quality Retained
MMLU Pro See original card Run benchmarks ~97-99%
HellaSwag See original card Run benchmarks ~97-99%
ARC Challenge See original card Run benchmarks ~97-99%
TruthfulQA See original card Run benchmarks ~97-99%
GSM8K See original card Run benchmarks ~97-99%

📦 Available Files

Filename Size RAM Required Quant Quality Best For
Qwen2.5-Coder-7B-Instruct-Q2_K.gguf 2.81 GB ~4.3 GB Q2_K ⭐ Extreme compression, significant quality loss.
Qwen2.5-Coder-7B-Instruct-Q3_K_L.gguf 3.81 GB ~5.3 GB Q3_K_L ⭐⭐⭐ Slightly better than Q3_K_M, still a compromise.
Qwen2.5-Coder-7B-Instruct-Q3_K_M.gguf 3.55 GB ~5.0 GB Q3_K_M ⭐⭐⭐ Very small file. Quality drop noticeable.
Qwen2.5-Coder-7B-Instruct-Q3_K_S.gguf 3.25 GB ~4.8 GB Q3_K_S ⭐⭐ Very high compression, high quality loss.
Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf 4.36 GB ~5.9 GB Q4_K_M ✅ Recommended ⭐⭐⭐⭐ Best balance of size and quality. Recommended for most users.
Qwen2.5-Coder-7B-Instruct-Q4_K_S.gguf 4.15 GB ~5.7 GB Q4_K_S ⭐⭐⭐½ Good speed/size balance, slight quality loss.
Qwen2.5-Coder-7B-Instruct-Q5_K_M.gguf 5.07 GB ~6.6 GB Q5_K_M ⭐⭐⭐⭐½ Better quality than Q4, slightly larger. Great if you have the RAM.
Qwen2.5-Coder-7B-Instruct-Q5_K_S.gguf 4.95 GB ~6.5 GB Q5_K_S ⭐⭐⭐⭐ Large but accurate.
Qwen2.5-Coder-7B-Instruct-Q6_K.gguf 5.82 GB ~7.3 GB Q6_K ⭐⭐⭐⭐⭐ Near-perfect quality, very large.
Qwen2.5-Coder-7B-Instruct-Q8_0.gguf 7.54 GB ~9.0 GB Q8_0 ⭐⭐⭐⭐⭐ Closest to original quality. Use when RAM is not a concern.

💡 Which file should I download?

  • Most users: Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf — best balance of size and quality
  • High RAM (32GB+): Qwen2.5-Coder-7B-Instruct-Q8_0.gguf — near-original quality
  • Low RAM (8GB): Qwen2.5-Coder-7B-Instruct-Q3_K_M.gguf — fits in 8GB with room to spare

⚡ Speed Benchmarks

Run python benchmark.py --model Qwen2.5-Coder-7B-Instruct to generate speed results.

🧠 Quality Benchmarks

Run kaggle_bench.ipynb on Kaggle to benchmark this model.

🚀 How to Use

Ollama

ollama run dhptl/qwen2.5-coder-7b-instruct

LM Studio / Jan / Open WebUI

Search for Dhptl/Qwen2.5-Coder-7B-Instruct in the model browser.

llama.cpp CLI

# Download the binary from https://github.com/ggerganov/llama.cpp/releases
./llama-cli \
  -m Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --conversation \
  -n 512

Python — llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="./Qwen2.5-Coder-7B-Instruct-Q4_K_M.gguf",
    n_gpu_layers=-1,   # -1 = offload everything to GPU
    n_ctx=4096,
)

response = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Tell me about quantization."}
])
print(response["choices"][0]["message"]["content"])

🔍 About GGUF Quantization

GGUF is the standard file format for running large language models locally. Quantization reduces the number of bits per weight:

Format Bits/weight Size vs FP16 Quality
Q2_K ~2.6 16% ⭐
Q3_K_M ~3.3 21% ⭐⭐⭐
Q4_K_M ~4.5 28% ⭐⭐⭐⭐ ← sweet spot
Q5_K_M ~5.6 35% ⭐⭐⭐⭐½
Q8_0 ~8.5 53% ⭐⭐⭐⭐⭐

💬 Community & Feedback

Found an issue? Have a question? Open a Discussion in the Community tab above.

If these quantizations were useful, please consider:

  • ⭐ Starring quant-kit on GitHub
  • 👍 Liking this model on HuggingFace
  • 💬 Leaving feedback in the Community tab