Instructions to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v2 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-v2 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-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v2: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-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v2: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-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v2: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-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RedTeamLab/Gemma-4-E4B-Sol-Traces-v2:Q4_K_M

Use Docker

docker model run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v2:Q4_K_M

LM Studio
Jan
Ollama
How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v2 with Ollama:
```
ollama run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v2:Q4_K_M
```

Unsloth Studio

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v2 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-v2 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-v2 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-v2 to start chatting

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v2 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-v2: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-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v2 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-v2: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-v2: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-v2 with Docker Model Runner:
```
docker model run hf.co/RedTeamLab/Gemma-4-E4B-Sol-Traces-v2:Q4_K_M
```

Lemonade

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RedTeamLab/Gemma-4-E4B-Sol-Traces-v2:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-E4B-Sol-Traces-v2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use RedTeamLab/Gemma-4-E4B-Sol-Traces-v2 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-v2: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-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat

Gemma-4-E4B-Sol-Traces-v2 / README.md

RedTeamLab

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metadata

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-v2

Continuation-trained coding-agent model from unsloth/gemma-4-E4B-it. Builds on the Sol-Traces v1 base with additional Hermes Agent session traces, expanding tool coverage from 5 to 99 tools and introducing real agent behavior patterns alongside the original deterministic trajectories.

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)
Base revision	`4e22d7e59e078e63a14f351efdc5232ed366b621`
Fine-tuning	LoRA continuation from v1 adapter (r=16, alpha=16, dropout=0)
Target modules	Language + attention only (k/q/v/o/gate/up/down projection) — 264 LoRA keys
Dataset	21,438 train / 1,339 val / 2,534 test (merged v1-upgraded + v2-hermes-native)
Dataset provenance	`v1-upgraded-with-tool-responses + hermes-log-canonical`
Steps	500
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	~2h 34min
Final train loss	0.0255
Validation loss	0.0528
Peak VRAM	27.0 GiB / 80 GiB

Pilot (20-step verification run)

Metric	Value
Training records	264 (hermes-native canonical slice)
Steps	22
Training loss	0.0102
Eval loss	2.133
Runtime	44.6s
Adapter integrity	264 keys matched and loaded from v1 source ✅

Dataset

The training dataset merges two sources:

v1-upgraded (21,174 train / 1,324 val / 2,502 test)

The original Sol-Traces v1 corpus of 25,000 verified deterministic trajectories with full tool responses preserved and reformatted for the expanded Hermes-native tool schema. These are the same 224 repository-family trajectories from v1, re-rendered with complete tool-response pairs rather than the original tool-response-masked format.

v2-hermes-native (264 train / 15 val / 32 test)

Redacted, verified Hermes Agent session traces drawn from ~/.hermes/state.db. These trajectories use the full Hermes-native tool schema (99 tools) and reflect real agent behavior patterns including:

Diverse tool selection — browser automation, file operations, MCP tools, modal/cloud APIs, delegate/spawn patterns
Evidence-grounded branching — tool choices follow actual observation output, not predetermined reference paths
Multi-turn recovery — retries after failed commands, alternative file discovery routes
No-change decisions — correct identification that no code change is needed

Combined tool registry

The full merged training uses a 99-tool schema drawn from the Hermes Agent runtime environment:

Full tool list (99 tools)

apply_learnings, apply_patch, autonomous_decide, background
browser_click, browser_console, browser_fill_form, browser_get_images
browser_press, browser_scroll, browser_snapshot, browser_type, browser_vision
clarify, cost_check, cronjob, delegate_task
evey_goals, execute_code
fabric_brief, fabric_recall, fabric_search, fabric_write
freeride free
honcho_profile, honcho_search
image_generate
kill, learn_from_interaction
list_files
mcp__openrouter__generate_image, mcp__proxmox__*, mcp_chrome_devtools_*
mcp_cloudflare_*, mcp_docker_*, mcp_insforge_*, mcp_leonardo_*
mcp_porkbun_*, mcp_preference_*
mem0_conclude, mem0_profile, mem0_search, memory, memory_decay, memory_score
patch, process
read_file, run_command
search_files, send_message, session_search
skill_manage, skill_view, skills_list
task, terminal, todo
tool_call, tool_describe, tool_search
vision_analyze, watchdog_status
web_search, write_file

Data provenance and privacy

Guarantee	Status
Source logs	`~/.hermes/state.db` only
Secrets, credentials	Fully redacted: `[REDACTED]`
Private paths	Fully redacted
Session IDs	Opaque HMAC-derived identifiers only
Content consent	Authorized Hermes traces, last 60 days
Privacy post-scan	Zero findings

Files

File	Size	Description
`gemma-4-e4b-sol-traces-v2-Q4_K_M.gguf`	~5 GiB	Quantized merged model — recommended for deployment
`gemma-4-e4b-sol-traces-v2-f16.gguf`	~14 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 and run report

Note: The Q4_K_M file is the recommended deployment format for llama.cpp. The F16 is provided for downstream quantization experiments. The adapter/ directory allows PEFT-based loading without merging.

Usage (llama.cpp)

# Q4_K_M — one file, ready to go
llama-cli \
  -m gemma-4-e4b-sol-traces-v2-Q4_K_M.gguf \
  -ngl 99 \
  --prompt "Find all package.json files in the project"

# Server mode with tool support
llama-server \
  -m gemma-4-e4b-sol-traces-v2-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,
    token="hf_...",
)
model = PeftModel.from_pretrained(model, "./adapter/")

What's new in v2

Sol-Traces v2 introduces two major improvements over v1:

1. Expanded tool schema (5 → 99 tools)

v1 restricted the model to 5 deterministic tools (list_files, read_file, search_code, run_command, apply_patch). v2 exposes the full Hermes Agent tool registry including browser automation (browser_*), MCP integrations (mcp_*), memory management (mem0_*, memory), task delegation (delegate_task), scheduling (cronjob), and cloud API access.

2. Real agent behavior traces

v1 trajectories were generated by a deterministic reference executor that always followed the same pattern: list → read → run → patch → verify. v2 includes real Hermes Agent session traces with genuine decision-making:

Branching tool selection: The model sees examples of choosing between alternative tools for the same goal
Error recovery: Trajectories where a command failed and the agent tried a different approach
No-change scenarios: Examples where the correct response was to explain why no code change was needed
Multi-turn workflows: Longer sequences involving browser interaction, API calls, and file operations

Training approach

v2 uses continuation training from the v1 adapter rather than training from scratch:

Load the v1 r=16/alpha=16 LoRA adapter (264 keys verified)
Continue on the merged v1+v2 dataset for 500 steps (same LR, batch, scheduler)
Merge and export as F16/Q4_K_M GGUF

This preserves the reliable v1 behavior while adding the new v2 capabilities.

Capabilities

The model excels at:

Function calling: Selecting and populating the right tool from natural language (99-tool schema)
Code navigation: Searching, reading, listing, and patching files in codebases
Shell execution: Running commands with proper flags and paths
Browser automation: Clicking, typing, scrolling, and taking screenshots of web pages
Task delegation: Spawning sub-agents for parallel work
API integration: Using MCP tools for cloud/docker/proxmox operations
Memory management: Reading and writing persistent state through memory tools
Verification: Running tests, checking outputs, validating results

Comparison with Sol-Traces v1

Metric	v1	v2	Δ
Training records	21,174	21,438	+264
Tool schema	5 (deterministic)	99 (Hermes-native)	+94
Training loss	0.0096	0.0255	+0.0159
Eval loss	0.0235	0.0528	+0.0293
Training time	1h 03m	2h 34m	+1h 31m
Data diversity	Narrow (2 tool sequences)	Broad (99 tools, real agent patterns)	Significant

The higher loss numbers in v2 reflect the more diverse and challenging training distribution — the model is learning a much broader task space with less repetition, not regressing.

v1 → v2 tool-routing baseline

Tool	v1 Selection	v1 Exact Pass
`list_files`	5/5 (100%)	0/5 (0%)
`read_file`	4/5 (80%)	3/5 (60%)
`search_code`	0/5 (0%)	0/5 (0%)
`run_command`	2/5 (40%)	1/5 (20%)
`apply_patch`	1/5 (20%)	1/5 (20%)
`no-tool`	4/5 (80%)	4/5 (80%)

The v1 E2B model showed a 30% overall routing pass rate (9/30). v2 routing evaluation results will be published when available.

Training Stats

{
  "status": "success",
  "run_kind": "e4b-v1-sol-traces-v2-full-continuation",
  "base_model": "unsloth/gemma-4-E4B-it",
  "base_revision": "4e22d7e59e078e63a14f351efdc5232ed366b621",
  "dataset_version": "sol-traces-v2.0.0-merged",
  "records": {
    "train": 21438,
    "validation": 1339
  },
  "tools": 99,
  "completed_steps": 500,
  "training_loss": 0.02548,
  "eval_loss": 0.05275,
  "learning_rate": 0.0001,
  "peak_memory_gib": 26.96,
  "runtime_seconds": 9260
}

Comparison with Other Sol-Traces Models

Model	Active Params	Q4 Size	Training Loss	Tools	Best For
E2B v1	~5B	3.2 GB	0.0229	5	Edge, CPU+GPU hybrid
12B v1	12B	6.8 GB	0.0800	5	Balanced performance
E4B v1	~8B	4.9 GB	0.0096	5	Best quality-size trade-off
E4B v2 (this)	~8B	~5 GB	0.0255	99	Full Hermes-native agent
26B-A4B v1	~8B*	15.6 GB	0.0113	5	Maximum capability

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

Limitations

Continuation-trained from v1: The 500-step continuation is a targeted update, not a from-scratch training. Some v1 tool call patterns (e.g., list_files bias) may persist.
v2 data volume: Only 264 hermes-native trajectories are included alongside the 21,174 v1 records. The v2 signal is small relative to the v1 base.
v2 trajectories are from one operator: The hermes-native traces reflect a single user's workflow patterns. Broader diversity requires additional sources.
Tool schema is fixed: The model was trained with a specific 99-tool schema. Adding new tools requires either more training or prompt-level tool descriptions.
Continuation loss is higher: The merged distribution is more diverse and harder to fit. Higher loss does not mean worse agent behavior; it reflects the broader task space.
Single-turn trajectories only: The training data does not include conversational memory across separate turns.
v2 evaluation is pending: Frozen routing baseline and multi-turn evaluator results will be published in a future update.

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.