Qwen3.5-9B-GLM5.1-Distill-v1 — GGUF Quantized
Overview
Qwen3.5-9B-GLM5.1-Distill-v1-GGUF is a GGUF release for llama.cpp-compatible runtimes and local inference, published by groxaxo.
It is intended for open-source evaluation, reproducible experimentation, and compatible local or
hosted inference workflows. The wording below is deliberately limited to what can be verified
from this repository's metadata and artifacts.
At a glance
| Field | Details |
|---|---|
| Format | GGUF |
| Source / base | Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1 |
| Intended task | the task described by the included configuration and documentation |
| License | apache-2.0 |
What is included
*.gguf(13 files)- Additional configuration, tokenizer, processor, or shard files (14 visible artifacts total)
Quick start
llama.cpp
Download a .gguf file that fits your available memory, then run it with a current llama.cpp
build:
llama-cli \
-m /path/to/model.gguf \
-p "Write a concise technical summary."
For vision or any-to-any models, download the matching multimodal projection file when one is provided and follow the source model's modality-specific instructions.
Compatibility and responsible use
- Use a runtime that explicitly supports this format, architecture, and modality.
- Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
- Review the source model card and license before redistribution or deployment.
- Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
- Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.
Quantization or conversion changes numerical behavior, memory use, and throughput relative to the source checkpoint; validate quality on your own workload.
Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.
All quantizations were produced using an importance matrix (imatrix) computed on 128 chunks of WikiText-2-raw-v1 for optimal quality at every bit-width.
Quantization Variants
| Variant | Size | BPW | Notes |
|---|---|---|---|
| F16 | 17.0 GB | 16.00 | Lossless half-precision |
| Q8_0 | 8.9 GB | 8.50 | Near-lossless, 8-bit round quant |
| Q6_K | 6.9 GB | 6.57 | 6-bit K-quant, excellent quality |
| Q5_K_M | 6.1 GB | 5.77 | 5-bit K-medium, recommended sweet spot |
| Q5_K_S | 5.9 GB | 5.62 | 5-bit K-small, slight size savings |
| Q4_K_M | 5.3 GB | 5.02 | 4-bit K-medium, best quality/size ratio |
| Q4_K_S | 5.0 GB | 4.77 | 4-bit K-small, good balance |
| IQ4_XS | 4.9 GB | 4.63 | 4-bit importance matrix, extra small |
| Q3_K_M | 4.4 GB | 4.12 | 3-bit K-medium |
| IQ3_M | 4.2 GB | 3.94 | 3-bit importance matrix, medium |
| Q3_K_S | 4.0 GB | 3.80 | 3-bit K-small |
| IQ3_XXS | 3.7 GB | 3.51 | 3-bit importance matrix, extra-extra small |
| Q2_K | 3.6 GB | 3.41 | 2-bit K-quant, smallest size |
Recommendations
- Best overall:
Q4_K_MorQ5_K_M— excellent quality-to-size ratio - Maximum quality:
Q6_KorQ8_0 - Tight VRAM:
IQ4_XSorQ3_K_M - Minimum size:
Q2_K
Usage
Compatible with llama.cpp, LM Studio, Ollama, text-generation-webui, and any GGUF-compatible inference engine.
llama-cli -m Qwen3.5-9B-GLM5.1-Distill-v1-Q4_K_M.gguf -p "Hello, world!"
Source Model
Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1 — a Qwen3.5-9B variant distilled with GLM5.1.