Instructions to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-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 ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

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 ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

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 ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

Use Docker

docker model run hf.co/ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

LM Studio
Jan
Ollama
How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0
```

Unsloth Studio

How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-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 ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-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 ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF to start chatting

How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

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": "ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0
```

Lemonade

How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

Run and chat with the model

lemonade run user.gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF-Q4_0

List all available models

lemonade list

Hermes Agent

How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-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 ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

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 ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0

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 "ji-farthing/gemma-4-qat-q4_0-MTP-assistants-ik-llama-GGUF:Q4_0" \
  --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"

ji-farthing commited on Jun 5

Commit

c3a9682

verified ·

1 Parent(s): bbcd153

Remove E2B and E4B assistants

Browse files

Files changed (1) hide show

README.md +0 -17

README.md CHANGED Viewed

@@ -1,8 +1,6 @@
 ---
 license: apache-2.0
 base_model:
-  - google/gemma-4-E2B-it-qat-q4_0-unquantized-assistant
-  - google/gemma-4-E4B-it-qat-q4_0-unquantized-assistant
   - google/gemma-4-12B-it-qat-q4_0-unquantized-assistant
   - google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant
   - google/gemma-4-31B-it-qat-q4_0-unquantized-assistant
@@ -28,8 +26,6 @@ the matching Google Gemma 4 QAT Q4_0 target GGUF.
 | Model | Q4_0 assistant | Q8_0 assistant |
 | --- | --- | --- |
-| Gemma 4 E2B IT QAT Q4_0 | `gemma-4-E2B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-E2B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
-| Gemma 4 E4B IT QAT Q4_0 | `gemma-4-E4B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-E4B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
 | Gemma 4 12B IT QAT Q4_0 | `gemma-4-12B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-12B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
 | Gemma 4 26B-A4B IT QAT Q4_0 | `gemma-4-26B-A4B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-26B-A4B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
 | Gemma 4 31B IT QAT Q4_0 | `gemma-4-31B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-31B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
@@ -40,8 +36,6 @@ BF16 conversion intermediates are not published here.
 Use the assistants with Google's official target GGUFs:
-- `google/gemma-4-E2B-it-qat-q4_0-gguf`
-- `google/gemma-4-E4B-it-qat-q4_0-gguf`
 - `google/gemma-4-12B-it-qat-q4_0-gguf`
 - `google/gemma-4-26B-A4B-it-qat-q4_0-gguf`
 - `google/gemma-4-31B-it-qat-q4_0-gguf`
@@ -67,8 +61,6 @@ Local conversion metadata checks:
 | Model | Tensors | Backbone | Centroid tensors |
 | --- | ---: | ---: | --- |
-| E2B | 50 | 1536 | yes |
-| E4B | 50 | 2560 | yes |
 | 12B | 48 | 3840 | no, metadata only |
 | 26B-A4B | 48 | 2816 | no, metadata only |
 | 31B | 48 | 5376 | no, metadata only |
@@ -82,19 +74,11 @@ Runtime smoke on an RTX 4070 with `ik_llama` build `4561 (6b9de3dba)`:
 | 12B | passed | MTP context ready; raw completion generated |
 | 26B-A4B | passed | MTP context ready; raw completion generated with CPU/system-RAM offload |
 | 31B | passed | MTP context ready; raw completion generated with CPU/system-RAM offload |
-| E2B | target-side load failed | Official target GGUF failed before draft load: missing `blk.15.attn_k.weight` in this `ik_llama` build |
-| E4B | target-side load failed | Official target GGUF failed before draft load: missing `blk.24.attn_k.weight` in this `ik_llama` build |
-The E2B/E4B assistant GGUFs are included because they convert and validate
-correctly, but current target-side `ik_llama` support for those official GGUFs
-may need additional work.
 ## Conversion Notes
 Source assistant repos:
-- `google/gemma-4-E2B-it-qat-q4_0-unquantized-assistant`
-- `google/gemma-4-E4B-it-qat-q4_0-unquantized-assistant`
 - `google/gemma-4-12B-it-qat-q4_0-unquantized-assistant`
 - `google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant`
 - `google/gemma-4-31B-it-qat-q4_0-unquantized-assistant`
@@ -104,4 +88,3 @@ converter, then quantized with `llama-quantize` to `Q4_0` and `Q8_0`.
 The `Q4_0` files are the intended pairing for the QAT Q4_0 targets. The `Q8_0`
 files are provided as higher-fidelity draft/reference variants.

 ---
 license: apache-2.0
 base_model:
   - google/gemma-4-12B-it-qat-q4_0-unquantized-assistant
   - google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant
   - google/gemma-4-31B-it-qat-q4_0-unquantized-assistant
 | Model | Q4_0 assistant | Q8_0 assistant |
 | --- | --- | --- |
 | Gemma 4 12B IT QAT Q4_0 | `gemma-4-12B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-12B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
 | Gemma 4 26B-A4B IT QAT Q4_0 | `gemma-4-26B-A4B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-26B-A4B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
 | Gemma 4 31B IT QAT Q4_0 | `gemma-4-31B-it-qat-q4_0-MTP-ik_llama-Q4_0.gguf` | `gemma-4-31B-it-qat-q4_0-MTP-ik_llama-Q8_0.gguf` |
 Use the assistants with Google's official target GGUFs:
 - `google/gemma-4-12B-it-qat-q4_0-gguf`
 - `google/gemma-4-26B-A4B-it-qat-q4_0-gguf`
 - `google/gemma-4-31B-it-qat-q4_0-gguf`
 | Model | Tensors | Backbone | Centroid tensors |
 | --- | ---: | ---: | --- |
 | 12B | 48 | 3840 | no, metadata only |
 | 26B-A4B | 48 | 2816 | no, metadata only |
 | 31B | 48 | 5376 | no, metadata only |
 | 12B | passed | MTP context ready; raw completion generated |
 | 26B-A4B | passed | MTP context ready; raw completion generated with CPU/system-RAM offload |
 | 31B | passed | MTP context ready; raw completion generated with CPU/system-RAM offload |
 ## Conversion Notes
 Source assistant repos:
 - `google/gemma-4-12B-it-qat-q4_0-unquantized-assistant`
 - `google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant`
 - `google/gemma-4-31B-it-qat-q4_0-unquantized-assistant`
 The `Q4_0` files are the intended pairing for the QAT Q4_0 targets. The `Q8_0`
 files are provided as higher-fidelity draft/reference variants.