Update to v2 adapter (1,546 samples, 2 epochs, loss 0.98)

README.md

@@ -1,209 +1,106 @@
 ---
+language:
+- ja
+- en
+license: apache-2.0
 base_model: google/gemma-4-31b-it
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:google/gemma-4-31b-it
-- lora
-- sft
-- transformers
-- trl
+- gemma4
+- code
+- agent
+- japanese
+- qlora
+- react
+- mcp
+- claude-code
+datasets:
+- custom
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-
-### Out-of-Scope Use
+# gemma4-31b-ja-agent-coder
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+**Japanese-enhanced agentic coding model** — Fine-tuned gemma4-31b-it for autonomous coding agents with Japanese language support.
 
-[More Information Needed]
+## Highlights
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **Agentic behavior**: ReAct reasoning, multi-step tool calling, self-correction
+- **Japanese coding**: Code generation, review, debugging in Japanese
+- **Claude Code compatible**: Designed as a local subagent for Claude Code via MCP
+- **Function calling**: Native Ollama/OpenAI tool use format
+- **Zero API cost**: Runs locally on 20GB+ VRAM
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
-
-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-### Framework versions
-
-- PEFT 0.18.2.dev0
+| Parameter | Value |
+|-----------|-------|
+| Base model | google/gemma-4-31b-it |
+| Method | QLoRA (4-bit NF4) |
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| Target modules | q/k/v/o_proj, gate/up/down_proj |
+| Trainable params | 133M / 31B (0.43%) |
+| Training data | 1,500+ custom samples |
+| Epochs | 3 |
+| Learning rate | 2e-4 (cosine) |
+| Hardware | NVIDIA RTX PRO 6000 (96GB VRAM) |
+
+## Training Data Categories
+
+| Category | Samples | Description |
+|----------|---------|-------------|
+| ReAct Tool Calling | ~120 | Single/chained tool calls |
+| Multi-step Agentic Trajectory | ~100 | Plan→Tool→Observe→Correct→Answer loops |
+| Self-correction | ~40 | Error recovery patterns |
+| Function Calling | ~50 | Ollama native tool format |
+| Japanese Code Generation | ~200 | JP instruction → Python/TS code |
+| Japanese Code Review | ~100 | Security, refactoring, best practices |
+| Japanese Error Explanation | ~80 | Error → JP diagnosis + fix |
+| Japanese Comprehension | ~50 | Reading, reasoning, summarization |
+| Debugging & Troubleshooting | ~100 | Error analysis → root cause → fix |
+| Git & CI/CD | ~80 | Branch strategy, PR, GitHub Actions |
+| Project Planning | ~80 | Requirements → task decomposition |
+| Technical Documentation | ~80 | README, API docs, specs |
+| Algorithms & Data Structures | ~200 | Binary search, DP, graph, sorting |
+| Web Frameworks | ~200 | FastAPI, Django, React, Next.js |
+| Database Operations | ~150 | SQLAlchemy, PostgreSQL, Redis |
+| Testing & DevOps | ~150 | pytest, Docker, K8s, Terraform |
+
+## Use with Ollama
+
+```bash
+ollama create gemma4-ja-agent-coder -f Modelfile
+ollama run gemma4-ja-agent-coder
+```
+
+## Use with helix-agents (Claude Code MCP)
+
+```json
+{
+  "mcpServers": {
+    "helix-agents": {
+      "command": "uv",
+      "args": ["run", "--directory", "/path/to/helix-agent", "python", "server.py"]
+    }
+  }
+}
+```
+
+## Use with transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base = AutoModelForCausalLM.from_pretrained("google/gemma-4-31b-it")
+model = PeftModel.from_pretrained(base, "tsunamayo7/gemma4-31b-ja-agent-coder")
+tokenizer = AutoTokenizer.from_pretrained("tsunamayo7/gemma4-31b-ja-agent-coder")
+```
+
+## License
+
+Apache 2.0 (same as base model)
+
+## Author
+
+[tsunamayo7](https://github.com/tsunamayo7)

adapter_config.json

@@ -19,29 +19,27 @@
   "lora_alpha": 32,
   "lora_bias": false,
   "lora_dropout": 0.05,
-  "lora_ga_config": null,
   "megatron_config": null,
   "megatron_core": "megatron.core",
   "modules_to_save": null,
   "peft_type": "LORA",
-  "peft_version": "0.18.2.dev0@e7355a3b2233820f6f30e558ce133ed22673a087",
+  "peft_version": "0.18.1",
   "qalora_group_size": 16,
   "r": 16,
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "up_proj",
-    "k_proj",
-    "gate_proj",
-    "v_proj",
-    "down_proj",
     "q_proj",
-    "o_proj"
+    "v_proj",
+    "gate_proj",
+    "k_proj",
+    "up_proj",
+    "o_proj",
+    "down_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",
   "trainable_token_indices": null,
-  "use_bdlora": null,
   "use_dora": false,
   "use_qalora": false,
   "use_rslora": false

adapter_model.safetensors

@@ -1,3 +1,3 @@
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 size 267146328