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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Hermes Agent

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

Gemma-4-E4B-Sol-Traces-v4 / README.md

RedTeamLab

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	---
	license: gemma
	language:
	- en
	tags:
	- agentic
	- tool-calling
	- function-calling
	- code-agent
	- gemma-4
	- e4b
	- lora
	- unsloth
	- sol-traces
	- hermes-agent
	base_model: unsloth/gemma-4-E4B-it
	library_name: gguf
	inference: false
	---

	# 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

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

	---
	license: gemma
	language:
	- en
	tags:
	- agentic
	- tool-calling
	- function-calling
	- code-agent
	- gemma-4
	- e4b
	- lora
	- unsloth
	- sol-traces
	- hermes-agent
	base_model: unsloth/gemma-4-E4B-it
	library_name: gguf
	inference: false
	---

	# 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

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