Instructions to use sokann/Qwen3.5-27B-GGUF-4.915bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sokann/Qwen3.5-27B-GGUF-4.915bpw 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-4.915bpw",
	filename="Qwen3.5-27B-GGUF-4.915bpw-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-4.915bpw 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-4.915bpw
# Run inference directly in the terminal:
llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.915bpw

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-4.915bpw
# Run inference directly in the terminal:
llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.915bpw

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-4.915bpw
# Run inference directly in the terminal:
./llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.915bpw

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-4.915bpw
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sokann/Qwen3.5-27B-GGUF-4.915bpw

Use Docker

docker model run hf.co/sokann/Qwen3.5-27B-GGUF-4.915bpw

LM Studio
Jan
Ollama
How to use sokann/Qwen3.5-27B-GGUF-4.915bpw with Ollama:
```
ollama run hf.co/sokann/Qwen3.5-27B-GGUF-4.915bpw
```

Unsloth Studio

How to use sokann/Qwen3.5-27B-GGUF-4.915bpw 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-4.915bpw 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-4.915bpw 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-4.915bpw to start chatting

How to use sokann/Qwen3.5-27B-GGUF-4.915bpw 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-4.915bpw

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-4.915bpw"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sokann/Qwen3.5-27B-GGUF-4.915bpw 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-4.915bpw

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-4.915bpw

Run Hermes

hermes

Docker Model Runner
How to use sokann/Qwen3.5-27B-GGUF-4.915bpw with Docker Model Runner:
```
docker model run hf.co/sokann/Qwen3.5-27B-GGUF-4.915bpw
```

Lemonade

How to use sokann/Qwen3.5-27B-GGUF-4.915bpw with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sokann/Qwen3.5-27B-GGUF-4.915bpw

Run and chat with the model

lemonade run user.Qwen3.5-27B-GGUF-4.915bpw-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-27B-GGUF-4.915bpw

This is a 4.915 BPW quantized model for the GPU poors with 24 GiB of VRAM. It uses the SOTA IQK quants, and thus works in ik_llama.cpp only.

From local testing with llama-perplexity, it has the best quality compared to the quants tested in https://www.reddit.com/r/LocalLLaMA/comments/1rk5qmr/qwen3527b_q4_quantization_comparison/, while being 1 GiB smaller than UD-Q4_K_XL.

There are 2 variants, one without imatrix, and one with imatrix from mradermacher.

With 24 GiB of VRAM, we can fit a context size of 131072 with F16 KV cache:

-c 131072 -ub 256

or a context size of 262144 with quantized KV cache:

-c 262144 -ctk q8_0 -ctv q6_0 -khad

Size

Size from llama-server output:

llm_load_print_meta: model size       = 15.391 GiB (4.915 BPW)
llm_load_print_meta: repeating layers = 13.744 GiB (4.848 BPW, 24.353 B parameters)
...
llm_load_tensors:  CUDA_Host buffer size =   682.03 MiB
llm_load_tensors:      CUDA0 buffer size = 15077.86 MiB

Quality

Recipe

blk\..*\.attn_q\.weight=iq6_k
blk\..*\.attn_k\.weight=iq6_k
blk\..*\.attn_v\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k
blk\..*\.attn_gate\.weight=iq6_k
blk\..*\.attn_qkv\.weight=iq5_k

blk\..*\.ssm_alpha\.weight=q8_0
blk\..*\.ssm_beta\.weight=q8_0
blk\..*\.ssm_out\.weight=iq6_k

blk\..*\.ffn_down\.weight=iq4_ks
blk\..*\.ffn_(gate|up)\.weight=iq4_ks

token_embd\.weight=iq4_k
output\.weight=iq6_k

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

Mean PPL(Q)                   :   6.965793 ±   0.048592
Mean PPL(base)                :   6.799430 ±   0.046581
Cor(ln(PPL(Q)), ln(PPL(base))):  98.14%
...
Mean    KLD:   0.064612 ±   0.001992
...
RMS Δp    :  5.419 ± 0.085 %
Same top p: 94.219 ± 0.061 %

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

Mean PPL(Q)                   :   6.749744 ±   0.046150
Mean PPL(base)                :   6.799430 ±   0.046581
Cor(ln(PPL(Q)), ln(PPL(base))):  98.46%
...
Mean    KLD:   0.054023 ±   0.001835
...
RMS Δp    :  5.385 ± 0.090 %
Same top p: 94.866 ± 0.057 %

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: 5

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-4.915bpw

Base model

Qwen/Qwen3.5-27B

Quantized

(198)

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