AnkitAI / text-generation updated 1 month ago

Parable-Qwen3-8B-Claude-Fable-5-GGUF

> ~6 GB of RAM is all you need. Laptop, mid-range GPU, yesterday's desktop — the Q4 build runs > anywhere with that much headroom. One command and you have a private, offline reasoning model on your machine: > > bash > ollama run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF:Q4KM >

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Downloads 30d
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Commercial use: allowed apache-2.0 Not gated GGUF 1 languages View on Hugging Face ↗

Download history

daily snapshots · 37 days
▲ 333 K in the last 30 days (128.3%)
603 K181 K
Aug 15Aug 27Sep 8Sep 20

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
Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q4_K_M.gguf Q4_K_M 5.0 GB 6.0 GB ✅ Runs comfortably
Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q5_K_M.gguf Q5_K_M 5.9 GB 6.9 GB ✅ Runs comfortably
Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q6_K.gguf Q6_K 6.7 GB 7.9 GB ✅ Runs comfortably
Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q8_0.gguf Q8_0 8.7 GB 10.1 GB ✅ Runs comfortably
Parable-Qwen3-8B-Claude-Fable-5-GGUF-F16.gguf GGUF 16.4 GB 18.5 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_K_M
$ ollama run parable-qwen3-8b-claude-fable-5-gguf

# pin the quantization explicitly
$ ollama run parable-qwen3-8b-claude-fable-5-gguf-q4_k_m
est. VRAM 6.0 GBon RTX 4090 · 24 GBJSON API →

Specifications

Licence
apache-2.0
First seen on the Hub
2026-07-14
Base model
Qwen3-8B
Training datasets
Glint-Research/Fable-5-traces, Roman1111111/gpt5.5-terminal
Added to our catalog
2026-08-15

Family

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

Compare with any text-generation model