Instructions to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RedTeamLab/Gemma-4-E4B-Sol-Traces-v4",
	filename="gemma-4-e4b-sol-traces-v4-Q4_K_M.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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4: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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4: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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M

Use Docker

docker model run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M

LM Studio
Jan
Ollama
How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with Ollama:
```
ollama run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M
```

Unsloth Studio

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 to start chatting

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4: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": "RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4: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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v4: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 "RedTeamLab/Gemma-4-E4B-Sol-Traces-v4: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 RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M
```

Lemonade

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Gemma-4-E4B-Sol-Traces-v4:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-E4B-Sol-Traces-v4-Q4_K_M

List all available models

lemonade list

Gemma-4-E4B-Sol-Traces-v4

Targeted routing-repair continuation from v3. Continuation-trained on 125 mined trajectories (SWE-bench, synthetic, bash history, GitHub commits) to fix run_command, search_code, and apply_patch tool selection.

Training

Parameter	Value
Base model	`unsloth/gemma-4-E4B-it`
Adapter start	v3 from-scratch (336 LoRA keys)
Training type	Continuation (100 steps)
Dataset	125 train / 15 val / 17 test
Sources	SWE-bench (50), synthetic (50), bash history (55), GitHub (2)
Tool schema	6 tools (search_code, run_command, apply_patch, read_file, list_files, write_file)
Learning rate	3e-5, cosine, 3% warmup
Batch	1 × 8 grad accum
Runtime	12 min on H100
Training loss	0.093
Eval loss	0.671
Cost	~$0.80

Routing Improvement

Tool	v1	v2	v3	v4
run_command	2/5	0/5	0/5	5/5 ✅
search_code	0/5	0/5	0/5	1/5
apply_patch	1/5	0/5	0/5	0/5
list_files	5/5	5/5	5/5	4/5
read_file	4/5	5/5	5/5	5/5
no-tool	4/5	5/5	5/5	3/5
Overall	53%	50%	50%	60%

Files

File	Size
`gemma-4-e4b-sol-traces-v4-Q4_K_M.gguf`	4.97 GiB
`gemma-4-e4b-sol-traces-v4-f16.gguf`	14.02 GiB
`adapter/adapter_model.safetensors`	35 MiB
`training_stats.json`	—

Usage

llama-cli -m gemma-4-e4b-sol-traces-v4-Q4_K_M.gguf -ngl 99

Downloads last month: 66

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

Inference Providers NEW

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Model tree for RedTeamLab/Gemma-4-E4B-Sol-Traces-v4

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

unsloth/gemma-4-E4B-it

Adapter

(58)

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