Text Generation
GGUF
Mixture of Experts
apex
quantized
granite
mamba
hybrid
llama.cpp
imatrix
conversational
Instructions to use Myric/granite-4.0-h-tiny-APEX-GGUF 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 Myric/granite-4.0-h-tiny-APEX-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 Myric/granite-4.0-h-tiny-APEX-GGUF # Run inference directly in the terminal: llama cli -hf Myric/granite-4.0-h-tiny-APEX-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Myric/granite-4.0-h-tiny-APEX-GGUF # Run inference directly in the terminal: llama cli -hf Myric/granite-4.0-h-tiny-APEX-GGUF
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 Myric/granite-4.0-h-tiny-APEX-GGUF # Run inference directly in the terminal: ./llama-cli -hf Myric/granite-4.0-h-tiny-APEX-GGUF
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 Myric/granite-4.0-h-tiny-APEX-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf Myric/granite-4.0-h-tiny-APEX-GGUF
Use Docker
docker model run hf.co/Myric/granite-4.0-h-tiny-APEX-GGUF
- LM Studio
- Jan
- vLLM
How to use Myric/granite-4.0-h-tiny-APEX-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Myric/granite-4.0-h-tiny-APEX-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Myric/granite-4.0-h-tiny-APEX-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Myric/granite-4.0-h-tiny-APEX-GGUF
- Ollama
How to use Myric/granite-4.0-h-tiny-APEX-GGUF with Ollama:
ollama run hf.co/Myric/granite-4.0-h-tiny-APEX-GGUF
- Unsloth Desktop
- Pi
How to use Myric/granite-4.0-h-tiny-APEX-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Myric/granite-4.0-h-tiny-APEX-GGUF
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Myric/granite-4.0-h-tiny-APEX-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Myric/granite-4.0-h-tiny-APEX-GGUF with Docker Model Runner:
docker model run hf.co/Myric/granite-4.0-h-tiny-APEX-GGUF
- Lemonade
How to use Myric/granite-4.0-h-tiny-APEX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Myric/granite-4.0-h-tiny-APEX-GGUF
Run and chat with the model
lemonade run user.granite-4.0-h-tiny-APEX-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use Myric/granite-4.0-h-tiny-APEX-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 Myric/granite-4.0-h-tiny-APEX-GGUF
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 Myric/granite-4.0-h-tiny-APEX-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Myric/granite-4.0-h-tiny-APEX-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Myric/granite-4.0-h-tiny-APEX-GGUF
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 "Myric/granite-4.0-h-tiny-APEX-GGUF" \ --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"
Upload generate_config.sh with huggingface_hub
Browse files- generate_config.sh +217 -0
generate_config.sh
ADDED
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| 1 |
+
# Vendored from localai-org/apex-quant @ a445a12 (MIT, (c) Ettore Di Giacinto).
|
| 2 |
+
# https://github.com/localai-org/apex-quant — see NOTICE. Unmodified.
|
| 3 |
+
#!/usr/bin/env bash
|
| 4 |
+
#
|
| 5 |
+
# generate_config.sh — Generate APEX tensor-type configuration files
|
| 6 |
+
#
|
| 7 |
+
# Creates a tensor-type file for llama-quantize's --tensor-type-file flag.
|
| 8 |
+
# Supports any number of layers and all APEX profiles.
|
| 9 |
+
#
|
| 10 |
+
# Usage:
|
| 11 |
+
# ./scripts/generate_config.sh --profile balanced --layers 40 > config.txt
|
| 12 |
+
# ./scripts/generate_config.sh --profile mini --layers 40 -o configs/my_config.txt
|
| 13 |
+
# ./scripts/generate_config.sh --custom --edge-exp Q6_K --mid-exp Q4_K \
|
| 14 |
+
# --shared Q8_0 --attn Q6_K --layers 40 > config.txt
|
| 15 |
+
#
|
| 16 |
+
# Profiles:
|
| 17 |
+
# quality Q6_K/Q5_K/IQ4_XS experts, Q8_0 shared, Q6_K attn
|
| 18 |
+
# i-quality Same as quality (use with --imatrix at quantize time)
|
| 19 |
+
# balanced Q6_K/Q5_K experts, Q8_0 shared, Q6_K attn
|
| 20 |
+
# i-balanced Same as balanced (use with --imatrix at quantize time)
|
| 21 |
+
# compact Q4_K/Q3_K experts, Q6_K shared, Q4_K attn
|
| 22 |
+
# i-compact Same as compact (use with --imatrix at quantize time)
|
| 23 |
+
# mini Q3_K edge / IQ2_S mid experts, Q5_K/Q4_K shared, Q4_K/Q3_K attn
|
| 24 |
+
# nano Q3_K edge / IQ2_S near / IQ2_XXS mid experts (2.06 bpw mid) — needs imatrix
|
| 25 |
+
# micro Q3_K edge / IQ2_XS near / IQ1_M mid experts (1.75 bpw mid) — needs imatrix, experimental
|
| 26 |
+
# custom Specify each type manually via flags
|
| 27 |
+
#
|
| 28 |
+
set -euo pipefail
|
| 29 |
+
|
| 30 |
+
# Defaults
|
| 31 |
+
PROFILE=""
|
| 32 |
+
LAYERS=40
|
| 33 |
+
OUTPUT=""
|
| 34 |
+
DENSE_LAYERS=0 # leading dense (non-MoE) FFN layers, e.g. LFM2-MoE
|
| 35 |
+
# Custom mode overrides
|
| 36 |
+
EDGE_EXP="" NEAR_EXP="" MID_EXP=""
|
| 37 |
+
EDGE_SHARED="" MID_SHARED=""
|
| 38 |
+
EDGE_ATTN="" MID_ATTN=""
|
| 39 |
+
|
| 40 |
+
show_help() {
|
| 41 |
+
sed -n '3,25p' "$0"
|
| 42 |
+
exit 0
|
| 43 |
+
}
|
| 44 |
+
|
| 45 |
+
while [ $# -gt 0 ]; do
|
| 46 |
+
case "$1" in
|
| 47 |
+
--profile|-p) PROFILE="$2"; shift 2 ;;
|
| 48 |
+
--layers|-l) LAYERS="$2"; shift 2 ;;
|
| 49 |
+
--dense-layers) DENSE_LAYERS="$2"; shift 2 ;;
|
| 50 |
+
--output|-o) OUTPUT="$2"; shift 2 ;;
|
| 51 |
+
--custom) PROFILE="custom"; shift ;;
|
| 52 |
+
--edge-exp) EDGE_EXP="$2"; shift 2 ;;
|
| 53 |
+
--near-exp) NEAR_EXP="$2"; shift 2 ;;
|
| 54 |
+
--mid-exp) MID_EXP="$2"; shift 2 ;;
|
| 55 |
+
--edge-shared) EDGE_SHARED="$2"; shift 2 ;;
|
| 56 |
+
--mid-shared) MID_SHARED="$2"; shift 2 ;;
|
| 57 |
+
--edge-attn) EDGE_ATTN="$2"; shift 2 ;;
|
| 58 |
+
--mid-attn) MID_ATTN="$2"; shift 2 ;;
|
| 59 |
+
--help|-h) show_help ;;
|
| 60 |
+
*) echo "Unknown option: $1" >&2; exit 1 ;;
|
| 61 |
+
esac
|
| 62 |
+
done
|
| 63 |
+
|
| 64 |
+
[ -z "$PROFILE" ] && { echo "Error: --profile required" >&2; exit 1; }
|
| 65 |
+
|
| 66 |
+
# Edge boundaries (first/last N layers get higher precision)
|
| 67 |
+
EDGE_HI=4 # L0..EDGE_HI
|
| 68 |
+
EDGE_LO=$(( LAYERS - 5 )) # EDGE_LO..LAYERS-1
|
| 69 |
+
NEAR_HI=9 # EDGE_HI+1..NEAR_HI
|
| 70 |
+
NEAR_LO=$(( LAYERS - 10 )) # NEAR_LO..EDGE_LO-1
|
| 71 |
+
|
| 72 |
+
# Set types per profile
|
| 73 |
+
case "$PROFILE" in
|
| 74 |
+
quality|i-quality)
|
| 75 |
+
EDGE_EXP="${EDGE_EXP:-Q6_K}"
|
| 76 |
+
NEAR_EXP="${NEAR_EXP:-Q5_K}"
|
| 77 |
+
MID_EXP="${MID_EXP:-iq4_xs}"
|
| 78 |
+
EDGE_SHARED="${EDGE_SHARED:-Q8_0}"
|
| 79 |
+
MID_SHARED="${MID_SHARED:-Q8_0}"
|
| 80 |
+
EDGE_ATTN="${EDGE_ATTN:-Q6_K}"
|
| 81 |
+
MID_ATTN="${MID_ATTN:-Q6_K}"
|
| 82 |
+
;;
|
| 83 |
+
balanced|i-balanced)
|
| 84 |
+
EDGE_EXP="${EDGE_EXP:-Q6_K}"
|
| 85 |
+
NEAR_EXP="${NEAR_EXP:-Q5_K}"
|
| 86 |
+
MID_EXP="${MID_EXP:-Q5_K}"
|
| 87 |
+
EDGE_SHARED="${EDGE_SHARED:-Q8_0}"
|
| 88 |
+
MID_SHARED="${MID_SHARED:-Q8_0}"
|
| 89 |
+
EDGE_ATTN="${EDGE_ATTN:-Q6_K}"
|
| 90 |
+
MID_ATTN="${MID_ATTN:-Q6_K}"
|
| 91 |
+
;;
|
| 92 |
+
compact|i-compact)
|
| 93 |
+
EDGE_EXP="${EDGE_EXP:-Q4_K}"
|
| 94 |
+
NEAR_EXP="${NEAR_EXP:-Q3_K}"
|
| 95 |
+
MID_EXP="${MID_EXP:-Q3_K}"
|
| 96 |
+
EDGE_SHARED="${EDGE_SHARED:-Q6_K}"
|
| 97 |
+
MID_SHARED="${MID_SHARED:-Q6_K}"
|
| 98 |
+
EDGE_ATTN="${EDGE_ATTN:-Q4_K}"
|
| 99 |
+
MID_ATTN="${MID_ATTN:-Q4_K}"
|
| 100 |
+
;;
|
| 101 |
+
mini)
|
| 102 |
+
EDGE_EXP="${EDGE_EXP:-Q3_K}"
|
| 103 |
+
NEAR_EXP="${NEAR_EXP:-Q3_K}"
|
| 104 |
+
MID_EXP="${MID_EXP:-iq2_s}"
|
| 105 |
+
EDGE_SHARED="${EDGE_SHARED:-Q5_K}"
|
| 106 |
+
MID_SHARED="${MID_SHARED:-Q4_K}"
|
| 107 |
+
EDGE_ATTN="${EDGE_ATTN:-Q4_K}"
|
| 108 |
+
MID_ATTN="${MID_ATTN:-Q3_K}"
|
| 109 |
+
;;
|
| 110 |
+
nano|i-nano)
|
| 111 |
+
# APEX Nano — aggressive mid-layer routed experts at IQ2_XXS (2.06 bpw)
|
| 112 |
+
# Target: ~25-30% smaller than Mini at modest quality cost. Requires imatrix.
|
| 113 |
+
EDGE_EXP="${EDGE_EXP:-Q3_K}"
|
| 114 |
+
NEAR_EXP="${NEAR_EXP:-iq2_s}"
|
| 115 |
+
MID_EXP="${MID_EXP:-iq2_xxs}"
|
| 116 |
+
EDGE_SHARED="${EDGE_SHARED:-Q5_K}"
|
| 117 |
+
MID_SHARED="${MID_SHARED:-Q4_K}"
|
| 118 |
+
EDGE_ATTN="${EDGE_ATTN:-Q4_K}"
|
| 119 |
+
MID_ATTN="${MID_ATTN:-Q3_K}"
|
| 120 |
+
;;
|
| 121 |
+
micro|i-micro)
|
| 122 |
+
# APEX Micro — extreme mid-layer routed experts at IQ1_M (1.75 bpw)
|
| 123 |
+
# Only viable on MoE: sparse expert activation + shared expert kept high-precision
|
| 124 |
+
# softens per-token error. Quality drop expected — experimental tier. Requires imatrix.
|
| 125 |
+
EDGE_EXP="${EDGE_EXP:-Q3_K}"
|
| 126 |
+
NEAR_EXP="${NEAR_EXP:-iq2_xs}"
|
| 127 |
+
MID_EXP="${MID_EXP:-iq1_m}"
|
| 128 |
+
EDGE_SHARED="${EDGE_SHARED:-Q5_K}"
|
| 129 |
+
MID_SHARED="${MID_SHARED:-Q4_K}"
|
| 130 |
+
EDGE_ATTN="${EDGE_ATTN:-Q4_K}"
|
| 131 |
+
MID_ATTN="${MID_ATTN:-Q3_K}"
|
| 132 |
+
;;
|
| 133 |
+
custom)
|
| 134 |
+
[ -z "$EDGE_EXP" ] && { echo "Error: --custom requires --edge-exp" >&2; exit 1; }
|
| 135 |
+
[ -z "$MID_EXP" ] && MID_EXP="$EDGE_EXP"
|
| 136 |
+
[ -z "$NEAR_EXP" ] && NEAR_EXP="$EDGE_EXP"
|
| 137 |
+
[ -z "$EDGE_SHARED" ] && EDGE_SHARED="Q8_0"
|
| 138 |
+
[ -z "$MID_SHARED" ] && MID_SHARED="$EDGE_SHARED"
|
| 139 |
+
[ -z "$EDGE_ATTN" ] && EDGE_ATTN="Q6_K"
|
| 140 |
+
[ -z "$MID_ATTN" ] && MID_ATTN="$EDGE_ATTN"
|
| 141 |
+
;;
|
| 142 |
+
*)
|
| 143 |
+
echo "Error: unknown profile '$PROFILE'" >&2
|
| 144 |
+
echo "Available: quality, i-quality, balanced, i-balanced, compact, i-compact, mini, nano, i-nano, micro, i-micro, custom" >&2
|
| 145 |
+
exit 1
|
| 146 |
+
;;
|
| 147 |
+
esac
|
| 148 |
+
|
| 149 |
+
# Generate config
|
| 150 |
+
generate() {
|
| 151 |
+
for (( i=0; i<LAYERS; i++ )); do
|
| 152 |
+
# Expert type based on layer position
|
| 153 |
+
if (( i <= EDGE_HI || i >= EDGE_LO )); then
|
| 154 |
+
exp_type="$EDGE_EXP"
|
| 155 |
+
elif (( i <= NEAR_HI || i >= NEAR_LO )); then
|
| 156 |
+
exp_type="$NEAR_EXP"
|
| 157 |
+
else
|
| 158 |
+
exp_type="$MID_EXP"
|
| 159 |
+
fi
|
| 160 |
+
|
| 161 |
+
# Shared type based on layer position
|
| 162 |
+
if (( i <= EDGE_HI || i >= EDGE_LO )); then
|
| 163 |
+
shared_type="$EDGE_SHARED"
|
| 164 |
+
else
|
| 165 |
+
shared_type="$MID_SHARED"
|
| 166 |
+
fi
|
| 167 |
+
|
| 168 |
+
# Attention type based on layer position
|
| 169 |
+
if (( i <= 2 || i >= LAYERS - 3 )); then
|
| 170 |
+
attn_type="$EDGE_ATTN"
|
| 171 |
+
else
|
| 172 |
+
attn_type="$MID_ATTN"
|
| 173 |
+
fi
|
| 174 |
+
|
| 175 |
+
if (( i < DENSE_LAYERS )); then
|
| 176 |
+
# Leading dense (non-MoE) FFN layers — keep at shared (edge) precision.
|
| 177 |
+
# ".weight" suffix prevents the regex from also matching ffn_*_exps/ffn_gate_inp.
|
| 178 |
+
echo "blk.${i}.ffn_gate.weight=${shared_type}"
|
| 179 |
+
echo "blk.${i}.ffn_up.weight=${shared_type}"
|
| 180 |
+
echo "blk.${i}.ffn_down.weight=${shared_type}"
|
| 181 |
+
else
|
| 182 |
+
# Routed expert tensors (dominant cost in MoE)
|
| 183 |
+
echo "blk.${i}.ffn_gate_exps=${exp_type}"
|
| 184 |
+
echo "blk.${i}.ffn_up_exps=${exp_type}"
|
| 185 |
+
echo "blk.${i}.ffn_down_exps=${exp_type}"
|
| 186 |
+
fi
|
| 187 |
+
|
| 188 |
+
# Shared expert tensors (archs with shared experts, e.g. Qwen3-MoE)
|
| 189 |
+
echo "blk.${i}.ffn_gate_shexp=${shared_type}"
|
| 190 |
+
echo "blk.${i}.ffn_up_shexp=${shared_type}"
|
| 191 |
+
echo "blk.${i}.ffn_down_shexp=${shared_type}"
|
| 192 |
+
|
| 193 |
+
# Attention tensors (attention layers)
|
| 194 |
+
echo "blk.${i}.attn_q=${attn_type}"
|
| 195 |
+
echo "blk.${i}.attn_k=${attn_type}"
|
| 196 |
+
echo "blk.${i}.attn_v=${attn_type}"
|
| 197 |
+
echo "blk.${i}.attn_output=${attn_type}"
|
| 198 |
+
echo "blk.${i}.attn_gate=${attn_type}"
|
| 199 |
+
echo "blk.${i}.attn_qkv=${attn_type}"
|
| 200 |
+
|
| 201 |
+
# Short-convolution mixing tensors (LFM2 conv layers — attention-equivalent)
|
| 202 |
+
echo "blk.${i}.shortconv.in_proj=${attn_type}"
|
| 203 |
+
echo "blk.${i}.shortconv.out_proj=${attn_type}"
|
| 204 |
+
|
| 205 |
+
# SSM tensors (Mamba/hybrid archs)
|
| 206 |
+
echo "blk.${i}.ssm_alpha=${attn_type}"
|
| 207 |
+
echo "blk.${i}.ssm_beta=${attn_type}"
|
| 208 |
+
echo "blk.${i}.ssm_out=${attn_type}"
|
| 209 |
+
done
|
| 210 |
+
}
|
| 211 |
+
|
| 212 |
+
if [ -n "$OUTPUT" ]; then
|
| 213 |
+
generate > "$OUTPUT"
|
| 214 |
+
echo "Config written to: $OUTPUT ($(wc -l < "$OUTPUT") lines, $LAYERS layers)" >&2
|
| 215 |
+
else
|
| 216 |
+
generate
|
| 217 |
+
fi
|