Instructions to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RedTeamLab/Gemma-4-E2B-Sol-Traces-v1",
	filename="gemma-4-e2b-sol-traces-v1-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-E2B-Sol-Traces-v1 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-E2B-Sol-Traces-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E2B-Sol-Traces-v1: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-E2B-Sol-Traces-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E2B-Sol-Traces-v1: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-E2B-Sol-Traces-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Gemma-4-E2B-Sol-Traces-v1: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-E2B-Sol-Traces-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Gemma-4-E2B-Sol-Traces-v1:Q4_K_M

Use Docker

docker model run hf.co/RedTeamLab/Gemma-4-E2B-Sol-Traces-v1:Q4_K_M

LM Studio
Jan
Ollama
How to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 with Ollama:
```
ollama run hf.co/RedTeamLab/Gemma-4-E2B-Sol-Traces-v1:Q4_K_M
```

Unsloth Studio

How to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 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-E2B-Sol-Traces-v1 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-E2B-Sol-Traces-v1 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-E2B-Sol-Traces-v1 to start chatting

How to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 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-E2B-Sol-Traces-v1: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-E2B-Sol-Traces-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 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-E2B-Sol-Traces-v1: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-E2B-Sol-Traces-v1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 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-E2B-Sol-Traces-v1: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-E2B-Sol-Traces-v1: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-E2B-Sol-Traces-v1 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Gemma-4-E2B-Sol-Traces-v1:Q4_K_M
```

Lemonade

How to use RedTeamLab/Gemma-4-E2B-Sol-Traces-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Gemma-4-E2B-Sol-Traces-v1:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-E2B-Sol-Traces-v1-Q4_K_M

List all available models

lemonade list

Gemma-4-E2B-Sol-Traces-v1

Repository coding-agent model fine-tuned from unsloth/gemma-4-E2B-it using LoRA on 25,000 verified deterministic reference trajectories.

The E2B variant is the smallest model in this four-run family. End-to-end tool-use benchmark comparisons are not yet published.

Sol Traces denotes tool-use traces compiled from Hermes Agent session logs; the traces do not originate from OpenCode.

Training Details

Parameter	Value
Base model	`unsloth/gemma-4-E2B-it` (MoE, 2 active experts)
Fine-tuning	LoRA (r=16, alpha=16, dropout=0)
Target modules	Language + attention (k/q/v/o/gate/up/down projection)
Dataset	21,174 train / 1,324 val (gemma-4-native-tools format)
Dataset provenance	`original-synthetic` — 25,000 verified trajectories compiled from Hermes Agent session logs across 32,560 attempted scenarios
Epochs	1
Learning rate	1e-4, cosine scheduler with 3% warmup
Batch size	8 (2 × 4 gradient accumulation)
Max sequence	8,192 tokens
Loss type	Assistant-only (tool responses excluded from loss)
GPU	Modal H100 80GB
Training time	~45 min (pilot 3min + full 42min)
Final train loss	0.0229
Validation loss	0.0248
Peak VRAM	33.7 GiB / 80 GiB
Throughput	4,587 tok/s

These are reported run metrics; the canonical training_stats.json artifact is not currently published for E2B.

Dataset

The training dataset consists of 25,000 executable trajectories built by a deterministic scenario generator and replayed against generated repositories. It uses 224 language/task/variant repository families with repository-family-balanced splits:

21,174 training records
1,324 validation records
2,502 test records (see dataset_manifest.json)

Each trajectory is a full agent session containing:

System instruction: Repository coding agent with tool-use guidelines
User task: A well-scoped coding task from the deterministic fixture catalogue
Assistant tool calls: Multi-step function-calling sequences using 5 tools:
- list_files — glob-based file discovery
- read_file — line-range file reading
- search_code — regex code search (defined in the schema; not emitted by the v1 reference policy)
- run_command — allowlisted shell execution
- apply_patch — unified diff application
Tool responses: Output, exit codes, truncation markers
Verification: Post-task validation commands with pass/fail outcomes

Actual v1 task coverage

Type	Records
`debugging`	5,424
`feature`	4,709
`refactoring`	3,582
`testing`	3,607
`build_config`	3,269
`integration`	2,742
`documentation_review`	1,667

Repository fixtures cover TypeScript, JavaScript, Python, shell, configuration, Go, Rust, and JVM/Java.

Data generation and verification

Sol Traces are compiled from Hermes Agent session logs produced while running deterministic, seed-based coding scenarios through a reference executor. The scenarios define repository templates, task requirements, and verification commands; accepted records retain the corresponding tool-use events and verification outcomes. Records are included only when their configured post-task validation succeeds.

The v1 reference policy is intentionally narrow: it always lists files, reads the known implementation path, runs pre-patch verification, applies the reference patch, and reruns verification. search_code is included in the schema but has no v1 calls.

Key Statistics

Metric	Value
Trace source	Hermes Agent session logs (deterministic scenario generator + reference executor)
Attempted seeds	32,560
Accepted trajectories	25,000 (76.8% acceptance rate)
Rejections	5,872 structural duplicates + 316 verification failures
Provenance	`original-synthetic`
Repository families	224 language/task/variant families across 8 fixture categories

Files

File	Size	Description
`gemma-4-e2b-sol-traces-v1-Q4_K_M.gguf`	3.18 GiB	Quantized merged model (Q4_K_M) — recommended for deployment
`gemma-4-e2b-sol-traces-v1-f16.gguf`	8.64 GiB	Full F16 merged model — for custom quantization
`dataset_manifest.json`	—	Accepted-record counts, split ratios, and rejection summary

Note: The Q4_K_M file is the recommended deployment format. The F16 is provided for downstream quantization experiments.

Usage (llama.cpp)

# Q4_K_M — one file, ready to go
llama-cli \
  -m gemma-4-e2b-sol-traces-v1-Q4_K_M.gguf \
  -ngl 99 \
  --prompt "List the files in the repository matching *.py"

# With conversation template
llama-cli \
  -m gemma-4-e2b-sol-traces-v1-Q4_K_M.gguf \
  -ngl 99 \
  --temp 0.2 \
  --chat-template gemma \
  -p "Search the codebase for any TODO comments"

Capabilities

The model excels at:

Function calling: Selecting and populating the right tool from natural language
Code navigation: Searching, reading, and listing files to understand codebases
Shell execution: Running commands with proper flags and paths
Patch application: Making small, correct code changes via unified diffs
Deterministic verification flow: Reproducing the fixture failure, applying the reference patch, and rerunning configured checks
Verification: Running tests and validating changes

Comparison with Other Sol-Traces Models

Model	Active Params	Q4 Size	Training Loss	Speed	Best For
E2B (this)	~5B	3.2 GB	0.0229	Fastest	Edge, CPU+GPU hybrid, low-resource
12B Unified	12B	6.8 GB	0.0800	Fast	Balanced performance
E4B	~8B	4.9 GB	0.0096	Fast	Best quality-size trade-off
26B-A4B	~8B*	15.6 GB	0.0113	Moderate	Maximum capability

*E4B and 26B-A4B both activate 4 experts but have different base architectures (dedicated encoder vs unified).

Limitations

Fine-tuned for repository coding agent scenarios — general chat or creative writing may not benefit
Single-turn trajectories only — no conversational memory across separate turns
Tool schemas are fixed to the 5 tools in the training set
Trained on synthetic trajectories — real-world coding patterns may differ

Training Stats

{
  "training_loss": 0.0229,
  "eval_loss": 0.0248,
  "steps": 377,
  "train_tokens": 24,704,714,
  "peak_vram_gib": 33.7,
  "throughput_tok_s": 4587,
  "runtime": "44m 46s"
}

Disclaimer

Use at your own risk. This model is fine-tuned for coding-agent scenarios. The model owner accepts no liability for any damages or losses arising from its use. Users are responsible for compliance with applicable laws and regulations.

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Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

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Model tree for RedTeamLab/Gemma-4-E2B-Sol-Traces-v1

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

unsloth/gemma-4-E2B-it

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