Instructions to use anikifoss/GLM-4.5-Air-HQ4_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use anikifoss/GLM-4.5-Air-HQ4_K 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 anikifoss/GLM-4.5-Air-HQ4_K # Run inference directly in the terminal: llama cli -hf anikifoss/GLM-4.5-Air-HQ4_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf anikifoss/GLM-4.5-Air-HQ4_K # Run inference directly in the terminal: llama cli -hf anikifoss/GLM-4.5-Air-HQ4_K
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 anikifoss/GLM-4.5-Air-HQ4_K # Run inference directly in the terminal: ./llama-cli -hf anikifoss/GLM-4.5-Air-HQ4_K
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 anikifoss/GLM-4.5-Air-HQ4_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf anikifoss/GLM-4.5-Air-HQ4_K
Use Docker
docker model run hf.co/anikifoss/GLM-4.5-Air-HQ4_K
- LM Studio
- Jan
- vLLM
How to use anikifoss/GLM-4.5-Air-HQ4_K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "anikifoss/GLM-4.5-Air-HQ4_K" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "anikifoss/GLM-4.5-Air-HQ4_K", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/anikifoss/GLM-4.5-Air-HQ4_K
- Ollama
How to use anikifoss/GLM-4.5-Air-HQ4_K with Ollama:
ollama run hf.co/anikifoss/GLM-4.5-Air-HQ4_K
- Unsloth Studio
How to use anikifoss/GLM-4.5-Air-HQ4_K 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 anikifoss/GLM-4.5-Air-HQ4_K 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 anikifoss/GLM-4.5-Air-HQ4_K to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for anikifoss/GLM-4.5-Air-HQ4_K to start chatting
- Pi
How to use anikifoss/GLM-4.5-Air-HQ4_K with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf anikifoss/GLM-4.5-Air-HQ4_K
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": "anikifoss/GLM-4.5-Air-HQ4_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use anikifoss/GLM-4.5-Air-HQ4_K with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf anikifoss/GLM-4.5-Air-HQ4_K
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 "anikifoss/GLM-4.5-Air-HQ4_K" \ --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 anikifoss/GLM-4.5-Air-HQ4_K with Docker Model Runner:
docker model run hf.co/anikifoss/GLM-4.5-Air-HQ4_K
- Lemonade
How to use anikifoss/GLM-4.5-Air-HQ4_K with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull anikifoss/GLM-4.5-Air-HQ4_K
Run and chat with the model
lemonade run user.GLM-4.5-Air-HQ4_K-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use anikifoss/GLM-4.5-Air-HQ4_K with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf anikifoss/GLM-4.5-Air-HQ4_K
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 anikifoss/GLM-4.5-Air-HQ4_K
Run Hermes
hermes
- Atomic Chat
File size: 4,367 Bytes
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quantized_by: anikifoss
pipeline_tag: text-generation
base_model: zai-org/GLM-4.5-Air
license: mit
base_model_relation: quantized
tags:
- conversational
- gguf
- no_imatrix
---
# Model Card
High quality quantization of **GLM-4.5-Air** without using imatrix.
# Run
## ik_llama.cpp
See [this detailed guide](https://github.com/ikawrakow/ik_llama.cpp/discussions/258) on how to setup ik_llama and how to make custom quants.
```
./build/bin/llama-server \
--alias anikifoss/GLM-4.5-Air-HQ4_K \
--model /mnt/data/Models/anikifoss/GLM-4.5-Air-HQ4_K/GLM-4.5-Air-HQ4_K-00001-of-00002.gguf \
--no-mmap -rtr \
--temp 0.5 --top-k 0 --top-p 1.0 --min-p 0.1 --repeat-penalty 1.0 \
--ctx-size 116000 \
-ctk f16 -ctv f16 \
-fa \
-b 1024 -ub 1024 \
-fmoe \
--n-gpu-layers 99 \
--override-tensor exps=CPU \
--parallel 1 \
--threads 32 \
--threads-batch 64 \
--host 127.0.0.1 \
--port 8090
```
## llama.cpp
```
./build/bin/llama-server \
--alias anikifoss/GLM-4.5-Air-HQ4_K \
--model /mnt/data/Models/anikifoss/GLM-4.5-Air-HQ4_K/GLM-4.5-Air-HQ4_K-00001-of-00002.gguf \
--no-mmap \
--temp 0.5 --top-k 0 --top-p 1.0 --min-p 0.1 \
--ctx-size 116000 \
-ctk f16 -ctv f16 \
-fa \
--override-tensor exps=CPU \
-ngl 99 \
--jinja \
--parallel 1 \
--threads 32 \
--threads-batch 64 \
--host 127.0.0.1 \
--port 8090
```
## Quantization Recipe
Quantized with [ik_llama](https://github.com/ikawrakow/ik_llama.cpp), but should work with any GGUF compatible inference framework.
```bash
#!/usr/bin/env bash
custom="
blk\.0\.ffn_down\.weight=q8_0
blk\.0\.ffn_gate\.weight=q8_0
blk\.0\.ffn_up\.weight=q8_0
blk\.46\.nextn\.eh_proj\.weight=bf16
blk\.46\.nextn\.embed_tokens\.weight=q8_0
blk\.46\.nextn\.enorm\.weight=f32
blk\.46\.nextn\.hnorm\.weight=f32
blk\.46\.nextn\.shared_head_head\.weight=q8_0
blk\.46\.nextn\.shared_head_norm\.weight=f32
blk\.[0-9]\.attn_k\.bias=f32
blk\.[0-9]\.attn_k\.weight=q8_0
blk\.[0-9]\.attn_norm\.weight=f32
blk\.[0-9]\.attn_output\.weight=q8_0
blk\.[0-9]\.attn_q\.bias=f32
blk\.[0-9]\.attn_q\.weight=q8_0
blk\.[0-9]\.attn_v\.bias=f32
blk\.[0-9]\.attn_v\.weight=q8_0
blk\.[0-9]\.post_attention_norm\.weight=f32
blk\.[1-3][0-9]\.attn_k\.bias=f32
blk\.[1-3][0-9]\.attn_k\.weight=q8_0
blk\.[1-3][0-9]\.attn_norm\.weight=f32
blk\.[1-3][0-9]\.attn_output\.weight=q8_0
blk\.[1-3][0-9]\.attn_q\.bias=f32
blk\.[1-3][0-9]\.attn_q\.weight=q8_0
blk\.[1-3][0-9]\.attn_v\.bias=f32
blk\.[1-3][0-9]\.attn_v\.weight=q8_0
blk\.[1-3][0-9]\.post_attention_norm\.weight=f32
blk\.4[0-6]\.attn_k\.bias=f32
blk\.4[0-6]\.attn_k\.weight=q8_0
blk\.4[0-6]\.attn_norm\.weight=f32
blk\.4[0-6]\.attn_output\.weight=q8_0
blk\.4[0-6]\.attn_q\.bias=f32
blk\.4[0-6]\.attn_q\.weight=q8_0
blk\.4[0-6]\.attn_v\.bias=f32
blk\.4[0-6]\.attn_v\.weight=q8_0
blk\.4[0-6]\.post_attention_norm\.weight=f32
blk\.[1-9]\.exp_probs_b\.bias=f32
blk\.[1-9]\.ffn_down_exps\.weight=q8_0
blk\.[1-9]\.ffn_down_shexp\.weight=bf16
blk\.[1-9]\.ffn_gate_exps\.weight=q4_K
blk\.[1-9]\.ffn_gate_inp\.weight=f32
blk\.[1-9]\.ffn_gate_shexp\.weight=bf16
blk\.[1-9]\.ffn_up_exps\.weight=q4_K
blk\.[1-9]\.ffn_up_shexp\.weight=bf16
blk\.[1-3][0-9]\.exp_probs_b\.bias=f32
blk\.[1-3][0-9]\.ffn_down_exps\.weight=q8_0
blk\.[1-3][0-9]\.ffn_down_shexp\.weight=bf16
blk\.[1-3][0-9]\.ffn_gate_exps\.weight=q4_K
blk\.[1-3][0-9]\.ffn_gate_inp\.weight=f32
blk\.[1-3][0-9]\.ffn_gate_shexp\.weight=bf16
blk\.[1-3][0-9]\.ffn_up_exps\.weight=q4_K
blk\.[1-3][0-9]\.ffn_up_shexp\.weight=bf16
blk\.4[0-6]\.exp_probs_b\.bias=f32
blk\.4[0-6]\.ffn_down_exps\.weight=q8_0
blk\.4[0-6]\.ffn_down_shexp\.weight=bf16
blk\.4[0-6]\.ffn_gate_exps\.weight=q4_K
blk\.4[0-6]\.ffn_gate_inp\.weight=f32
blk\.4[0-6]\.ffn_gate_shexp\.weight=bf16
blk\.4[0-6]\.ffn_up_exps\.weight=q4_K
blk\.4[0-6]\.ffn_up_shexp\.weight=bf16
output\.weight=bf16
output_norm\.weight=f32
token_embd\.weight=bf16
"
custom=$(
echo "$custom" | grep -v '^#' | \
sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)
echo "Running with: -custom-q $custom"
TARGET_MODEL="GLM-4.5-Air-HQ4_K"
mkdir -p ~/Env/models/anikifoss/$TARGET_MODEL
./build/bin/llama-quantize \
--custom-q "$custom" \
/mnt/data/Models/zai-org/GLM-4.5-Air-GGUF/GLM-4.5-Air-128x9.4B-BF16-00001-of-00005.gguf \
~/Env/models/anikifoss/$TARGET_MODEL/$TARGET_MODEL.gguf \
Q4_K \
32
``` |