Instructions to use sokann/Qwen3.5-27B-GGUF-4.165bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sokann/Qwen3.5-27B-GGUF-4.165bpw",
	filename="Qwen3.5-27B-GGUF-4.165bpw-imatrix.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sokann/Qwen3.5-27B-GGUF-4.165bpw
# Run inference directly in the terminal:
llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.165bpw

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sokann/Qwen3.5-27B-GGUF-4.165bpw
# Run inference directly in the terminal:
llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.165bpw

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf sokann/Qwen3.5-27B-GGUF-4.165bpw
# Run inference directly in the terminal:
./llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.165bpw

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf sokann/Qwen3.5-27B-GGUF-4.165bpw
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.165bpw

Use Docker

docker model run hf.co/sokann/Qwen3.5-27B-GGUF-4.165bpw

LM Studio
Jan
Ollama
How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with Ollama:
```
ollama run hf.co/sokann/Qwen3.5-27B-GGUF-4.165bpw
```

Unsloth Studio

How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sokann/Qwen3.5-27B-GGUF-4.165bpw to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sokann/Qwen3.5-27B-GGUF-4.165bpw to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sokann/Qwen3.5-27B-GGUF-4.165bpw to start chatting

How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sokann/Qwen3.5-27B-GGUF-4.165bpw

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sokann/Qwen3.5-27B-GGUF-4.165bpw"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sokann/Qwen3.5-27B-GGUF-4.165bpw

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sokann/Qwen3.5-27B-GGUF-4.165bpw

Run Hermes

hermes

Docker Model Runner
How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with Docker Model Runner:
```
docker model run hf.co/sokann/Qwen3.5-27B-GGUF-4.165bpw
```

Lemonade

How to use sokann/Qwen3.5-27B-GGUF-4.165bpw with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sokann/Qwen3.5-27B-GGUF-4.165bpw

Run and chat with the model

lemonade run user.Qwen3.5-27B-GGUF-4.165bpw-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-27B-GGUF-4.165bpw

This is a 4.165 BPW quantized model for the GPU poors with 16 GiB of VRAM. It works in both ik_llama.cpp and mainline llama.cpp.

It was quantized following the old wisdom from https://github.com/ggml-org/llama.cpp/issues/1256#issuecomment-1535758958, specifically:

Quantize first 1/4, then every 3rd layer with more bits

The FFN tensors were quantized following this strategy, to either q4_K or q3_K.

PPL is very good, and the model also performs very well in actual Q&A and agentic tasks.

UPDATE: There are now 2 variants, the original one without imatrix, and a new one with imatrix from mradermacher. More llama-perplexity results added below.

Size

Size from llama-server output:

llm_load_print_meta: model size       = 13.040 GiB (4.165 BPW)
llm_load_print_meta: repeating layers = 11.708 GiB (4.130 BPW, 24.353 B parameters)
...
llm_load_tensors:  CUDA_Host buffer size =   682.03 MiB
llm_load_tensors:      CUDA0 buffer size = 12671.04 MiB

Quality

Recipe

blk\..*\.attn_q\.weight=q4_K
blk\..*\.attn_k\.weight=q4_K
blk\..*\.attn_v\.weight=q4_K
blk\..*\.attn_output\.weight=q4_K
blk\..*\.attn_gate\.weight=q4_K
blk\..*\.attn_qkv\.weight=q4_K

blk\..*\.ssm_alpha\.weight=q4_K
blk\..*\.ssm_beta\.weight=q4_K
blk\..*\.ssm_out\.weight=q4_K

blk\.(0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|18|21|24|27|30|33|36|39|42|45|48|51|54|57|60|63)\.ffn_(down|gate|up)\.weight=q4_K
blk\..*\.ffn_(down|gate|up)\.weight=q3_K

token_embd\.weight=q4_K
output\.weight=q4_K

PPL result with wiki.test.raw (no imatrix):

Final estimate: PPL over 580 chunks for n_ctx=512 = 6.8931 +/- 0.04448

~~This was quantized without using imatrix, because PPL is somehow worse with imatrix.~~

PPL result with wiki.test.raw (with imatrix from mradermacher):

Final estimate: PPL over 580 chunks for n_ctx=512 = 6.9863 +/- 0.04539

It looks like PPL alone is not a good enough metric for this model. As such, further test was done using the same methodology as https://www.reddit.com/r/LocalLLaMA/comments/1rk5qmr/qwen3527b_q4_quantization_comparison/. The quant did well enough to keep up, while being significantly smaller.

PPL/KLD/RMS result with wikitext2_test.txt (no imatrix):

Mean PPL(Q)                   :   6.501285 ±   0.042748
Mean PPL(base)                :   6.799430 ±   0.046581
Cor(ln(PPL(Q)), ln(PPL(base))):  95.92%
...
Mean    KLD:   0.135754 ±   0.002773
...
RMS Δp    :  8.422 ± 0.085 %
Same top p: 90.236 ± 0.077 %

PPL/KLD/RMS result with wikitext2_test.txt (with imatrix from mradermacher):

Mean PPL(Q)                   :   6.783163 ±   0.045910
Mean PPL(base)                :   6.799430 ±   0.046581
Cor(ln(PPL(Q)), ln(PPL(base))):  97.26%
...
Mean    KLD:   0.101915 ±   0.002372
...
RMS Δp    :  7.196 ± 0.081 %
Same top p: 91.563 ± 0.072 %

In general, llama-perplexity results are better with imatrix, but there is a possibility that imatrix will cause an unexpected token to be chosen in actual tasks (see https://huggingface.co/ubergarm/GLM-4.5-GGUF/discussions/3).

Downloads last month: 64

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for sokann/Qwen3.5-27B-GGUF-4.165bpw

Base model

Qwen/Qwen3.5-27B

Quantized

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