Instructions to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF",
	filename="Qwen3.5-27B-GLM5.1-Distill-v1-4A-4BIT-CHAMPION.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 Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

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 Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

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 Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with Ollama:
```
ollama run hf.co/Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF 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 Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF 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 Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF to start chatting

How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

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": "Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

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 Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M
```

Lemonade

How to use Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Fredred89/Qwen3.5-27B-GLM5.1-Distill-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-27B-GLM5.1-Distill-v1-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-27B-GLM5.1-Distill-v1-GGUF / README.md

Fredred89

Add README with dense model architecture info and APEX v5 details

e6d5a0d verified about 1 month ago

preview code

Raw

History Blame

3.21 kB

	---
	license: apache-2.0
	language:
	- en
	- zh
	tags:
	- gguf
	- qwen
	- apex
	- dense
	- quantized
	base_model: Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1
	---

	# Qwen3.5-27B-GLM5.1-Distill-v1 — APEX Quantized GGUF

	> Architecture: DENSE (NOT MoE) — All 27B parameters are active on every token. No expert routing.

	## Model Info

	\| Property \| Value \|
	\|---\|---\|
	\| Base model \| [Qwen3.5-27B-GLM5.1-Distill-v1](https://huggingface.co/Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1) \|
	\| Parameters \| 27B (all active, dense) \|
	\| Layers \| 64 (48 GDN recurrent + 16 full-attention, every 4th layer) \|
	\| Architecture \| Qwen3_5ForConditionalGeneration (hybrid GDN) \|
	\| Vocab \| 248,320 \|
	\| Context \| 262,144 \|

	## Available Quantizations

	\| File \| Type \| Size \| PPL \| Notes \|
	\|---\|---\|---\|---\|---\|
	\| `...-APEX-Quality-v5.gguf` \| APEX Q4_K_M + edge upgrades \| 16.18 GB \| 5.5596 \| Best quality — beats built-in Q4_K_M \|
	\| `...-Q4_K_M.gguf` \| Built-in Q4_K_M \| 15.41 GB \| 5.5687 \| Baseline \|
	\| `...-Q8_0.gguf` \| Q8_0 \| ~27 GB \| — \| High quality reference \|
	\| `...-F16-fixed2.gguf` \| F16 \| 53.8 GB \| ~5.55 \| Full precision (fixed block_count) \|

	Benchmark: wiki.test.raw, c=2048, chunks=10

	## APEX Quality v5 — Method

	This uses an APEX-inspired minimal-override strategy adapted for dense models.

	What APEX Quality v5 does:
	- 93 tensor-type-file overrides — only edge layer upgrades
	- Edge layers L0-7, L56-63: q4_K → q5_K
	- token_embd.weight: q4_K → q6_K
	- Everything else: no override (built-in k-quant mixture handles it)

	Why this works for dense models:
	- Unlike MoE models (where 97% of expert params are inactive per token), dense models have all parameters active on every forward pass
	- Built-in llama.cpp k-quant mixture is already near-optimal for dense models
	- Only edge layers benefit from upgrades (embedding alignment + logit generation)
	- Full-replacement APEX tiers perform worse on this dense model than minimal overrides

	Key difference from APEX paper:
	The original APEX paper targets MoE models (Qwen3.5-35B-A3B with 256 routed experts). Its biggest innovation — compressing inactive experts aggressively — doesn't apply here. We only use the layer-gradient principle.

	## Reproduction

	```bash
	# 1. Convert to F16 and fix metadata (block_count bug: 65 → 64)
	python3 convert_hf_to_gguf.py safetensors_source/ --outfile model-F16.gguf --outtype f16
	llama-quantize \
	--override-kv 'qwen35.block_count=int:64' \
	--override-kv 'qwen35.nextn_predict_layers=int:0' \
	model-F16.gguf model-F16-fixed.gguf COPY

	# 2. Quantize with APEX tensor-type-file (see APEX-Quality-v5.tensor_types.txt)
	llama-quantize \
	--tensor-type-file APEX-Quality-v5.tensor_types.txt \
	model-F16-fixed.gguf APEX-Quality-v5.gguf Q4_K_M

	# 3. Benchmark (MUST use c=2048, not default c=512)
	llama-perplexity -m APEX-Quality-v5.gguf -f wiki.test.raw -c 2048 --chunks 10 -t 4
	```

	## Credits

	- APEX methodology: [LocalAI/apex-quant](https://github.com/mudler/apex-quant) — Ettore Di Giacinto, Richard Palethorpe
	- Base model: [Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1](https://huggingface.co/Jackrong/Qwen3.5-27B-GLM5.1-Distill-v1)
	- Quantization: llama.cpp stock tooling, no custom kernels