RedTeamLab/Gemma-4-E2B-Sol-Traces-v1

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

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

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

Files

Note: The Q4KM 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

*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.