Dhptl/stable-code-3b-GGUF

stable-code-3b — GGUF Quantizations

Quantized GGUF versions of stabilityai/stable-code-3b

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

Quantized by Dhptl on June 18, 2026 using quant-kit

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⚖️ 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%

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📦 Available Files

Filename	Size	RAM Required	Quant	Quality	Best For
stable-code-3b-Q2_K.gguf	1.01 GB	~2.5 GB	Q2_K	⭐	Extreme compression, significant quality loss.
stable-code-3b-Q3_K_L.gguf	1.40 GB	~2.9 GB	Q3_K_L	⭐⭐⭐	Slightly better than Q3_K_M, still a compromise.
stable-code-3b-Q3_K_M.gguf	1.30 GB	~2.8 GB	Q3_K_M	⭐⭐⭐	Very small file. Quality drop noticeable.
stable-code-3b-Q3_K_S.gguf	1.17 GB	~2.7 GB	Q3_K_S	⭐⭐	Very high compression, high quality loss.
stable-code-3b-Q4_K_M.gguf	1.59 GB	~3.1 GB	Q4_K_M ✅ Recommended	⭐⭐⭐⭐	Best balance of size and quality. Recommended for most users.
stable-code-3b-Q4_K_S.gguf	1.51 GB	~3.0 GB	Q4_K_S	⭐⭐⭐½	Good speed/size balance, slight quality loss.
stable-code-3b-Q5_K_M.gguf	1.86 GB	~3.4 GB	Q5_K_M	⭐⭐⭐⭐½	Better quality than Q4, slightly larger. Great if you have the RAM.
stable-code-3b-Q5_K_S.gguf	1.81 GB	~3.3 GB	Q5_K_S	⭐⭐⭐⭐	Large but accurate.
stable-code-3b-Q6_K.gguf	2.14 GB	~3.6 GB	Q6_K	⭐⭐⭐⭐⭐	Near-perfect quality, very large.
stable-code-3b-Q8_0.gguf	2.77 GB	~4.3 GB	Q8_0	⭐⭐⭐⭐⭐	Closest to original quality. Use when RAM is not a concern.

💡 Which file should I download?

• Most users: stable-code-3b-Q4_K_M.gguf — best balance of size and quality
• High RAM (32GB+): stable-code-3b-Q8_0.gguf — near-original quality
• Low RAM (8GB): stable-code-3b-Q3_K_M.gguf — fits in 8GB with room to spare

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⚡ Speed Benchmarks

Run python benchmark.py --model stable-code-3b to generate speed results.

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🧠 Quality Benchmarks

Run kaggle_bench.ipynb on Kaggle to benchmark this model.

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🚀 How to Use

Ollama

ollama run dhptl/stable-code-3b

LM Studio / Jan / Open WebUI

Search for Dhptl/stable-code-3b in the model browser.

llama.cpp CLI

# Download the binary from https://github.com/ggerganov/llama.cpp/releases
./llama-cli \
  -m stable-code-3b-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="./stable-code-3b-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"])

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🔍 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%	⭐⭐⭐⭐⭐

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💬 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