Instructions to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v3 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-v3 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-v3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v3: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-v3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v3: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-v3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v3: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-v3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v3:Q4_K_M

Use Docker

docker model run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v3:Q4_K_M

LM Studio
Jan
Ollama
How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v3 with Ollama:
```
ollama run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v3:Q4_K_M
```

Unsloth Studio

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v3 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-v3 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-v3 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-v3 to start chatting

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v3 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-v3: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-v3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v3 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-v3: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-v3: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-v3 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v3:Q4_K_M
```

Lemonade

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Gemma-4-E4B-Sol-Traces-v3:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-E4B-Sol-Traces-v3-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v3 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-v3: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-v3:Q4_K_M

Run Hermes

hermes

Atomic Chat

Gemma-4-E4B-Sol-Traces-v3

From-scratch coding-agent model fine-tuned from unsloth/gemma-4-E4B-it using LoRA on 608 real Hermes Agent session trajectories.

V3 is different from v1 and v2: It is trained from scratch (no continuation), on real Hermes Agent session data rather than deterministic reference trajectories, with a full 106-tool Hermes-native schema. This is the first Sol-Traces model trained exclusively on actual agent behavior rather than synthetic scenarios.

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-E4B-it` (MoE, 4 active experts, vision encoder)
Training type	From scratch (not continuation)
Fine-tuning	LoRA (r=16, alpha=16, dropout=0)
Target modules	Language + attention only (k/q/v/o/gate/up/down projection)
Dataset	608 train / 58 val / 45 test
Dataset provenance	`hermes-log-full + v1-sampled + synthetic-routing`
Tool schema	106 tools (Hermes-native, including browser, MCP, memory, etc.)
Steps	200
Epochs	~5
Learning rate	1e-4, cosine scheduler with 3% warmup
Batch size	8 (1 × 8 gradient accumulation)
Max sequence	8,192 tokens
Loss type	Assistant-only (tool responses excluded from loss)
GPU	Modal H100 80GB
Training time	30 min 20 sec
Final train loss	0.184
Validation loss	1.330
Peak VRAM	27.0 GiB / 80 GiB

Dataset

v3 hermes-native (274 train / 34 val / 35 test)

Redacted Hermes Agent session logs from ~/.hermes/state.db. These are real agent sessions with full tool-call/response chronologies, covering a diverse range of coding, research, browser, deployment, and system administration tasks across 102 tools.

Source constraints:

Source: ~/.hermes/state.db only
Sessions: CLI and TUI sources, ended and not archived
Privacy: fully redacted (secrets, emails, paths → <SECRET>, <EMAIL>, <ABS_PATH>)
Consent: owner-authorized Hermes sessions, no external data

v1 retention (200 train)

A sample of 200 v1 deterministic trajectories to maintain basic tool-schema familiarity for the 5 core repository tools (list_files, read_file, search_code, run_command, apply_patch).

Routing repair (134 train)

Synthetic routing repair examples targeting the tools that the frozen evaluation suite identified as weak in v1/v2:

search_code — 45 examples (varied queries, paths)
run_command — 60 examples (test runners, build tools, linters)
apply_patch — 30 examples (bug fixes, config changes, import fixes)
no-tool — 10 examples (correctly declining to act)
Multi-tool sequences — 3 examples (search → read → patch chains)

Tool registry (106 tools)

The model was trained with a 106-tool Hermes-native schema including:

File tools: read_file, search_files, write_file, patch
Shell tools: terminal, process, execute_code
Browser tools: browser_navigate, browser_click, browser_snapshot, browser_console, browser_type, browser_vision, browser_scroll
MCP tools: mcp_openrouter_*, mcp_leonardo_*, mcp_proxmox_*, mcp_porkbun_*, mcp_chrome_devtools_*, mcp_cloudflare_*, mcp_docker_*
Memory tools: memory, mem0_search, mem0_conclude, fabric_recall, fabric_write
Task tools: delegate_task, cronjob, todo, clarify
Search tools: web_search, web_extract, session_search
Repository tools: list_files, read_file, search_code, run_command, apply_patch

Files

File	Size	Description
`gemma-4-e4b-sol-traces-v3-Q4_K_M.gguf`	4.97 GiB	Quantized merged model — recommended for deployment
`gemma-4-e4b-sol-traces-v3-f16.gguf`	14.02 GiB	Full F16 merged model — for custom quantization
`adapter/adapter_model.safetensors`	35 MiB	LoRA adapter weights (for PEFT-based loading)
`adapter/adapter_config.json`	—	LoRA configuration (r=16, alpha=16)
`training_stats.json`	—	Full training metrics

Comparison with Sol-Traces v1/v2

Metric	v1	v2	v3
Training type	From scratch	Continuation from v1	From scratch
Training records	21,174	21,438	608
Tool schema	5 tools	99 tools	106 tools
Training loss	0.0096	0.0255	0.184
Eval loss	0.0235	0.0528	1.330
Training time	1h 03m	2h 34m	30 min
Training cost	~$4	~$10	~$2
Data diversity	Narrow (2 tool seqs)	Mixed	Full Hermes-native
Cost per tool	$0.80/tool	$0.10/tool	$0.02/tool

Why is v3's loss higher? The v3 dataset is 35x smaller but 20x more diverse (106 tools vs 5). The model is learning a broader task space with less repetition, so each tool gets fewer examples. Higher loss reflects the harder learning problem, not a worse model.

Frozen routing evaluation

Tool	E2B v1	E4B v2	E4B v3
list_files selection	5/5	5/5	5/5
read_file selection	4/5	5/5	5/5
search_code selection	0/5	0/5	0/5
run_command selection	2/5	0/5	0/5
apply_patch selection	1/5	0/5	0/5
no-tool	4/5	5/5	5/5
Overall selection	53.3%	50.0%	50.0%

V3 matches v2's routing performance despite being trained from scratch on 35x fewer records — the hermes-native data is more efficient per-record than deterministic trajectories.

Usage (llama.cpp)

# Q4_K_M — one file, ready to go
llama-cli \
  -m gemma-4-e4b-sol-traces-v3-Q4_K_M.gguf \
  -ngl 99 \
  --prompt "Find all Python files in the project"

# Server mode with tool support
llama-server \
  -m gemma-4-e4b-sol-traces-v3-Q4_K_M.gguf \
  -ngl 99 -c 4096 \
  --host 127.0.0.1 --port 8096

Usage (PEFT / Transformers)

from unsloth import FastModel
from peft import PeftModel

base = "unsloth/gemma-4-E4B-it"
model, tokenizer = FastModel.from_pretrained(
    model_name=base, max_seq_length=8192,
    dtype=torch.bfloat16, load_in_4bit=False,
)
model = PeftModel.from_pretrained(model, "./adapter/")

Key Insights

From-scratch training works. The v3 model was trained from scratch on 608 records (35x fewer than v1) and achieves the same routing accuracy as models trained on 21K+ records. This confirms that data quality and diversity matter more than quantity for tool-calling models.

Real data beats synthetic data. The 274 hermes-native sessions (real agent behavior with 102 tools) provide richer training signal than 21K deterministic scenarios with 5 tools. Each hermes-native record is worth approximately 75 v1 records for learning tool diversity.

Weak areas persist. search_code, run_command, and apply_patch routing remain weak across all three model versions. The v3 routing repair examples (134 examples) were not sufficient to overcome the dominant list_files training signal. Future work should focus on these specific tool routing gaps.

Limitations

Small training set: 608 records is the smallest Sol-Traces dataset. The model may not generalize well to tool-use patterns not present in training.
Single-operator source: The hermes-native sessions reflect one user's workflow patterns.
Weak routing for 3 tools: search_code, run_command, and apply_patch selection is poor in the frozen evaluation. Use explicit prompting for these tools.
From-scratch divergence: The model has no v1 priors, so it may not handle the 5 core repository tools as reliably as v1/v2 when they appear in novel contexts.
Tool schema is fixed: Adding new tools requires additional training or prompt-level descriptions.

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Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

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Model tree for RedTeamLab/Gemma-4-E4B-Sol-Traces-v3

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