Upload Gemma 4 E4B fine-tuned for Basque GEC (merged 16-bit)

- QLoRA r=16 fine-tuned on horkonpon-corpus (199,662 records)
- 2 epochs, 24,958 steps, ~19h on NVIDIA L40
- F0.5=80.8 on horkonpon eval (1,088 records, 100% JSON parse)
- Beats GECToR v2 (F0.5=78.8) on recall + exact match
- Includes: model.safetensors, tokenizer, chat_template, eval_report, system_prompt

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,235 @@
+---
+license: gemma
+language:
+  - eu
+base_model:
+  - google/gemma-4-E4B-it
+tags:
+  - basque
+  - euskara
+  - grammatical-error-correction
+  - gec
+  - text-generation
+  - instruction-tuned
+  - explainable
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Gemma 4 E4B · Basque GEC (Explainable)
+
+Fine-tuned **Google Gemma 4 E4B** for **Basque (euskara) grammatical error correction** with structured, explainable JSON output. Trained on [horkonpon-corpus](https://github.com/itzune/horkonpon-corpus), an EBE-grounded, CC-BY-SA 4.0 Basque GEC training corpus.
+
+## Model details
+
+| | |
+|---|---|
+| **Base model** | [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it) (July 2026) |
+| **Architecture** | Gemma 4 E4B (PLE, 8B total / 4.5B effective params) |
+| **Fine-tuning** | QLoRA 4-bit + LoRA r=16, then merged to 16-bit |
+| **Modality** | Text-only (vision/audio components frozen, untrained) |
+| **Training data** | 199,662 records from horkonpon-corpus (error + clean + suggestion) |
+| **Output format** | Structured JSON (target sentence + per-edit metadata) |
+| **Languages** | Basque (eu) |
+| **License** | Gemma Terms of Use (base) · CC-BY-SA 4.0 (training data) |
+| **Hardware** | NVIDIA L40 (46 GB VRAM) |
+
+## Output format
+
+The model produces **Format 2** (explain-then-correct) JSON: a corrected sentence plus a list of edits, each annotated with category, nature (normative error vs. editorial suggestion), reference, and a Basque-language description.
+
+```json
+{
+  "target": "Argazkiak bidaltzen dituzuen guztion artean Errigorako bi otar zozkatuko ditugu.",
+  "edits": [
+    {
+      "src_content": "bidaltsen",
+      "tgt_content": "bidaltzen",
+      "category": "spelling",
+      "error_type": "char_swap",
+      "error_severity": 2,
+      "nature": "error",
+      "ref": "EBE id=1317",
+      "error_description": "«ts» eta «tz» nahasketa. Hemen «tz» da zuzena. Ordezkatu bidaltsen → bidaltzen."
+    }
+  ]
+}
+```
+
+### Fields
+
+| Field | Description |
+|---|---|
+| `target` | Corrected sentence |
+| `edits[].src_content` | Erroneous span in the source |
+| `edits[].tgt_content` | Corrected span |
+| `edits[].category` | Error category (see below) |
+| `edits[].nature` | `error` (EBE normative) or `suggestion` (Berria editorial) |
+| `edits[].ref` | Normative reference (EBE rule, araua, etc.) |
+| `edits[].error_description` | Human-readable explanation in Basque |
+
+### Categories
+
+`capitalization`, `word_level`, `spelling`, `zalantza` (word-choice), `terminology`, `morphology`, `punctuation`, `proper_noun`, `calque`
+
+### Runtime filtering
+
+The structured output lets the application filter at inference time:
+
+```python
+# Only apply EBE normative corrections, ignore Berria editorial suggestions
+edits = [e for e in output["edits"] if e["nature"] == "error"]
+
+# Or ignore a specific underperforming category
+edits = [e for e in output["edits"] if e["category"] != "calque"]
+```
+
+## Evaluation
+
+Evaluated on the horkonpon-corpus gold eval split: **1,088 errorful records** (1,037 error + 51 suggestion) and **1,088 clean records** (false-positive test). Greedy decoding, `max_new_tokens=256`, batch_size=32.
+
+### Headline results
+
+| Metric | GECToR-v2 | **Gemma 4** | Winner |
+|---|:---:|:---:|---|
+| **F0.5** | 78.8 | **80.8** | Gemma 4 |
+| Exact match | 52.7% | **65.5%** | Gemma 4 |
+| Precision | **88.7%** | 86.3% | GECToR-v2 |
+| Recall | 54.4% | **64.4%** | Gemma 4 |
+| Clean FP rate | **1.4%** | 8.6% | GECToR-v2 |
+| JSON parse rate | N/A | **100%** | — |
+
+### Per-category recall
+
+| Category | GECToR-v2 | **Gemma 4** | Notes |
+|---|:---:|:---:|---|
+| capitalization | 100% (33/33) | 100% (33/33) | tie |
+| word_level | 97.2% (35/36) | 100% (36/36) | |
+| spelling | 40.6% (69/170) | **90.6%** (154/170) | 2.2× improvement |
+| zalantza | 48.3% (43/89) | **92.1%** (82/89) | 1.9× improvement |
+| terminology | — | 88.2% (45/51) | suggestion-only |
+| morphology | **94.5%** (276/292) | 77.1% (225/292) | GECToR wins |
+| punctuation | 25.6% (66/258) | **72.9%** (188/258) | 2.8× improvement |
+| proper_noun | 22.9% (19/83) | **68.7%** (57/83) | 3.0× improvement |
+| calque | 6.6% (5/76) | 17.1% (13/76) | both weak |
+
+### Key findings
+
+1. **Gemma 4 beats GECToR v2 on F0.5** (+2.0) by trading precision for recall.
+2. **Dramatic gains on semantic categories** — spelling, zalantza, punctuation, proper_noun — where the LLM leverages world knowledge that GECToR's fixed edit vocabulary cannot.
+3. **GECToR v2 wins on morphology** (sequence tagging excels at fine-grained inflection) and is far more conservative on clean text (1.4% FP vs 8.6% — important for a writing tool).
+4. **Both models remain weak on calque** (lexical calques need more training pairs).
+5. **100% JSON parse rate** — structured output is fully reliable.
+
+## Training details
+
+| Parameter | Value |
+|---|---|
+| Method | QLoRA (4-bit NF4 quantization + LoRA r=16, α=16) |
+| Epochs | 2 |
+| Total steps | 24,958 |
+| Training time | ~19 hours (NVIDIA L40) |
+| Final train loss | 0.0145 |
+| Final eval loss | 0.0259 |
+| Batch size | 8 × 2 (gradient accumulation) = 16 effective |
+| Learning rate | 2e-4 |
+| Max sequence length | 1024 |
+| Optimizer | adamw_8bit |
+| Chat template | gemma-4 (non-thinking) |
+| Framework | Unsloth + transformers 5.5.0 |
+
+The LoRA adapter was merged into the base model in 16-bit precision for deployment (no inference-time adapter overhead).
+
+## Usage
+
+### With transformers
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "itzune/gemma-4-e4b-horkonpon",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = AutoProcessor.from_pretrained("itzune/gemma-4-e4b-horkonpon")
+tokenizer = processor.tokenizer
+
+system_prompt = (
+    "Zuzendu emandako euskara testuko akatsak, aldaketa minimalak eginez, "
+    "eta azaldu edizio bakoitua. Jarraitu honako arau hau: «EBE aditz-taulak "
+    "eta eranskina: aditz komunztadura, deklinabide-atxiki eta pertsona-izenordainak "
+    "EBEk ezarritako paradien arabera erabili». Eman emaitza JSON gisa: "
+    '{"target": "<testu zuzendua>", "edits": [{"src_content": "<jatorrizkoa>", '
+    '"tgt_content": "<zuzena>", "category": "...", "error_type": "...", '
+    '"error_severity": 1, "nature": "error", "ref": "EBE ...", '
+    '"error_description": "..."}]}."
+)
+user_input = "Argazkiak bidaltsen dituzuen guztion artean..."
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_input},
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text=prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(**inputs, max_new_tokens=256, do_sample=False)
+
+response = tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+print(response)  # JSON string
+```
+
+### Inference speed
+
+| Config | Throughput | VRAM |
+|---|---|---|
+| Merged 16-bit, batch 32 | ~219 tok/s | 17.9 GB |
+| 4-bit quantized, batch 32 | ~190 tok/s | 13.0 GB |
+
+For production, consider merging the LoRA adapter and using vLLM for 5–10× speedup.
+
+## Training data
+
+Trained on [**horkonpon-corpus**](https://github.com/itzune/horkonpon-corpus):
+
+- **208,051 records** (83,121 error + 88,009 clean + 36,921 suggestion)
+- **199,662 training / 1,088 eval**
+- EBE-grounded (*Euskara Batuaren Eskuliburua*, Euskaltzaindia's normative handbook)
+- 16 deterministic error injectors across 9 categories
+- CC-BY-SA 4.0 (commercially usable)
+- Clean text from [Latxa-Corpus-v2](https://huggingface.co/datasets/HiTZ/latxa-corpus-v2)
+
+## Limitations
+
+- **Clean FP rate (8.6%)** — the model sometimes "corrects" already-correct sentences. For a writing assistant where false positives are costly, GECToR v2 (1.4% FP) may be preferable, or apply a confidence threshold.
+- **Calque recall (17.1%)** — lexical calques remain the weakest category; more training pairs needed.
+- **Morphology (77.1%)** — lower than GECToR v2's sequence-tagging approach (94.5%). The LLM occasionally over-generalizes inflection rules.
+- **Basque-specific** — fine-tuned for Basque only; performance on other languages reflects the base model, not the fine-tune.
+- **Multimodal components untrained** — the vision/audio/video components are frozen and untested; use text-only.
+
+## Ethical considerations
+
+- The model corrects grammar but should not be used to gatekeep language use or discourage Basque learners.
+- EBE is the sole normative authority; `nature=suggestion` items are editorial (Berria style guide), not normative.
+- Training data is synthetic-error-injection based; the model may reflect biases in the source corpus (Latxa-Corpus-v2).
+
+## Citation
+
+```
+@misc{itzune-gemma4-e4b-horkonpon,
+  title  = {Gemma 4 E4B Fine-tuned for Basque Grammatical Error Correction},
+  author = {Itzune},
+  year   = {2025},
+  url    = {https://huggingface.co/itzune/gemma-4-e4b-horkonpon},
+}
+```
+
+## Related
+
+- **[horkonpon-corpus](https://github.com/itzune/horkonpon-corpus)** — the training corpus (CC-BY-SA 4.0)
+- **[gector-eus-v2](https://huggingface.co/itzune/gector-eus-v2)** — the GECToR model trained on the same corpus (F0.5=78.8)
+- **[txukun](https://github.com/itzune/txukun)** — the Basque writing assistant this model powers
+- **[google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it)** — the base model

chat_template.jinja

@@ -0,0 +1,385 @@
+{%- macro format_parameters(properties, required, filter_keys=false) -%}
+    {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
+    {%- set ns = namespace(found_first=false) -%}
+    {%- for key, value in properties | dictsort -%}
+        {%- set add_comma = false -%}
+        {%- if not filter_keys or key not in standard_keys -%}
+            {%- if ns.found_first %},{% endif -%}
+            {%- set ns.found_first = true -%}
+            {{ key }}:{
+            {%- if value['description'] -%}
+                description:<|"|>{{ value['description'] }}<|"|>
+                {%- set add_comma = true -%}
+            {%- endif -%}
+            {%- if value['type'] | upper == 'STRING' -%}
+                {%- if value['enum'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    enum:{{ format_argument(value['enum']) }}
+                {%- endif -%}
+            {%- elif value['type'] | upper == 'ARRAY' -%}
+                {%- if value['items'] is mapping and value['items'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    items:{
+                    {%- set ns_items = namespace(found_first=false) -%}
+                    {%- for item_key, item_value in value['items'] | dictsort -%}
+                        {%- if item_value is not none -%}
+                            {%- if ns_items.found_first %},{% endif -%}
+                            {%- set ns_items.found_first = true -%}
+                            {%- if item_key == 'properties' -%}
+                                properties:{
+                                {%- if item_value is mapping -%}
+                                    {{- format_parameters(item_value, value['items']['required'] | default([])) -}}
+                                {%- endif -%}
+                                }
+                            {%- elif item_key == 'required' -%}
+                                required:[
+                                {%- for req_item in item_value -%}
+                                    <|"|>{{- req_item -}}<|"|>
+                                    {%- if not loop.last %},{% endif -%}
+                                {%- endfor -%}
+                                ]
+                            {%- elif item_key == 'type' -%}
+                                {%- if item_value is string -%}
+                                    type:{{ format_argument(item_value | upper) }}
+                                {%- else -%}
+                                    type:{{ format_argument(item_value | map('upper') | list) }}
+                                {%- endif -%}
+                            {%- else -%}
+                                {{ item_key }}:{{ format_argument(item_value) }}
+                            {%- endif -%}
+                        {%- endif -%}
+                    {%- endfor -%}
+                    }
+                {%- endif -%}
+            {%- endif -%}
+            {%- if value['nullable'] %}
+                {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                nullable:true
+            {%- endif -%}
+            {%- if value['type'] | upper == 'OBJECT' -%}
+                {%- if value['properties'] is defined and value['properties'] is mapping -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    properties:{
+                    {{- format_parameters(value['properties'], value['required'] | default([])) -}}
+                    }
+                {%- elif value is mapping -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    properties:{
+                    {{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}}
+                    }
+                {%- endif -%}
+                {%- if value['required'] -%}
+                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+                    required:[
+                    {%- for item in value['required'] | default([]) -%}
+                        <|"|>{{- item -}}<|"|>
+                        {%- if not loop.last %},{% endif -%}
+                    {%- endfor -%}
+                    ]
+                {%- endif -%}
+            {%- endif -%}
+            {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
+            type:<|"|>{{ value['type'] | upper }}<|"|>}
+        {%- endif -%}
+    {%- endfor -%}
+{%- endmacro -%}
+{%- macro format_function_declaration(tool_data) -%}
+    declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|>
+    {%- set params = tool_data['function']['parameters'] -%}
+    {%- if params -%}
+        ,parameters:{
+        {%- if params['properties'] -%}
+            properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
+        {%- endif -%}
+        {%- if params['required'] -%}
+            required:[
+            {%- for item in params['required'] -%}
+                <|"|>{{- item -}}<|"|>
+                {{- ',' if not loop.last -}}
+            {%- endfor -%}
+            ],
+        {%- endif -%}
+        {%- if params['type'] -%}
+            type:<|"|>{{- params['type'] | upper -}}<|"|>}
+        {%- endif -%}
+    {%- endif -%}
+    {%- if 'response' in tool_data['function'] -%}
+        {%- set response_declaration = tool_data['function']['response'] -%}
+        ,response:{
+        {%- if response_declaration['description'] -%}
+            description:<|"|>{{- response_declaration['description'] -}}<|"|>,
+        {%- endif -%}
+        {%- if response_declaration['type'] | upper == 'OBJECT' -%}
+            type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>}
+        {%- endif -%}
+    {%- endif -%}
+    }
+{%- endmacro -%}
+{%- macro format_argument(argument, escape_keys=True) -%}
+    {%- if argument is none -%}
+        {{- 'null' -}}
+    {%- elif argument is string -%}
+        {{- '<|"|>' + argument + '<|"|>' -}}
+    {%- elif argument is boolean -%}
+        {{- 'true' if argument else 'false' -}}
+    {%- elif argument is mapping -%}
+        {{- '{' -}}
+        {%- set ns = namespace(found_first=false) -%}
+        {%- for key, value in argument | dictsort -%}
+            {%- if ns.found_first %},{% endif -%}
+            {%- set ns.found_first = true -%}
+            {%- if escape_keys -%}
+                {{- '<|"|>' + key + '<|"|>' -}}
+            {%- else -%}
+                {{- key -}}
+            {%- endif -%}
+            :{{- format_argument(value, escape_keys=escape_keys) -}}
+        {%- endfor -%}
+        {{- '}' -}}
+    {%- elif argument is sequence -%}
+        {{- '[' -}}
+        {%- for item in argument -%}
+            {{- format_argument(item, escape_keys=escape_keys) -}}
+            {%- if not loop.last %},{% endif -%}
+        {%- endfor -%}
+        {{- ']' -}}
+    {%- else -%}
+        {{- argument -}}
+    {%- endif -%}
+{%- endmacro -%}
+{%- macro strip_thinking(text) -%}
+    {%- set ns = namespace(result='') -%}
+    {%- for part in text.split('<channel|>') -%}
+        {%- if '<|channel>' in part -%}
+            {%- set ns.result = ns.result + part.split('<|channel>')[0] -%}
+        {%- else -%}
+            {%- set ns.result = ns.result + part -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {{- ns.result | trim -}}
+{%- endmacro -%}
+
+{%- macro format_tool_response_block(tool_name, response) -%}
+    {{- '<|tool_response>' -}}
+    {%- if response is mapping -%}
+        {{- 'response:' + tool_name + '{' -}}
+        {%- for key, value in response | dictsort -%}
+            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}
+            {%- if not loop.last %},{% endif -%}
+        {%- endfor -%}
+        {{- '}' -}}
+    {%- else -%}
+        {{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}}
+    {%- endif -%}
+    {{- '<tool_response|>' -}}
+{%- endmacro -%}
+
+{#- ===== SETUP ===== -#}
+{%- set ns = namespace(prev_message_type=None, prev_non_tool_role=None) -%}
+{%- set loop_messages = messages -%}
+{%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+{{- bos_token -}}
+{#- Handle System/Tool Definitions Block -#}
+{%- if enable_thinking or tools or (messages and messages[0]['role'] in ['system', 'developer']) -%}
+    {{- '<|turn>system\n' -}}
+    {#- Inject Thinking token at the very top of the FIRST system turn -#}
+    {%- if enable_thinking -%}
+        {{- '<|think|>\n' -}}
+        {%- set ns.prev_message_type = 'think' -%}
+    {%- endif -%}
+    {%- if messages and messages[0]['role'] in ['system', 'developer'] -%}
+        {%- if messages[0]['content'] is string -%}
+            {{- messages[0]['content'] | trim -}}
+        {%- elif messages[0]['content'] is sequence -%}
+            {%- for item in messages[0]['content'] -%}
+                {{- item['text'] | trim + ' '-}}
+            {%- endfor -%}
+        {%- endif -%}
+        {%- set loop_messages = messages[1:] -%}
+    {%- endif -%}
+    {%- if tools -%}
+        {%- for tool in tools %}
+            {{- '<|tool>' -}}
+            {{- format_function_declaration(tool) | trim -}}
+            {{- '<tool|>' -}}
+        {%- endfor %}
+        {%- set ns.prev_message_type = 'tool' -%}
+    {%- endif -%}
+    {{- '<turn|>\n' -}}
+{%- endif %}
+
+{#- Pre-scan: find last user message index for reasoning guard -#}
+{%- set ns_turn = namespace(last_user_idx=-1) -%}
+{%- for i in range(loop_messages | length) -%}
+    {%- if loop_messages[i]['role'] == 'user' -%}
+        {%- set ns_turn.last_user_idx = i -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{#- Loop through messages -#}
+{%- for message in loop_messages -%}
+    {%- if message['role'] != 'tool' -%}
+    {%- set ns.prev_message_type = None -%}
+    {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
+    {#- Detect continuation using tracked state — O(1) instead of O(n) backward scan -#}
+    {%- set continue_same_model_turn = (role == 'model' and ns.prev_non_tool_role == 'assistant') -%}
+    {%- if not continue_same_model_turn -%}
+        {{- '<|turn>' + role + '\n' }}
+    {%- endif -%}
+
+    {#- Render reasoning/reasoning_content as thinking channel -#}
+    {%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}
+    {%- set thinking_gate = (loop.index0 > ns_turn.last_user_idx) or (preserve_thinking and message.get('tool_calls')) -%}
+    {%- if thinking_text and thinking_gate -%}
+        {{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
+    {%- endif -%}
+
+            {%- if message.get('tool_calls') -%}
+                {%- for tool_call in message.get('tool_calls') -%}
+                    {%- set function = tool_call['function'] -%}
+                    {{- '<|tool_call>call:' + function['name'] + '{' -}}
+                    {%- if function['arguments'] is mapping -%}
+                        {%- set ns_args = namespace(found_first=false) -%}
+                        {%- for key, value in function['arguments'] | dictsort -%}
+                            {%- if ns_args.found_first %},{% endif -%}
+                            {%- set ns_args.found_first = true -%}
+                            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}
+                        {%- endfor -%}
+                    {%- elif function['arguments'] is none -%}
+                    {%- elif function['arguments'] is string -%}
+                        {#- Pre-serialized args (e.g. an OpenAI JSON string). We cannot JSON-parse
+                            portably in-template, so render non-fatally instead of erroring. Strip an
+                            outer {...} so it composes with the DSL braces rather than double-wrapping.
+                            Prefer passing arguments as a mapping for exact Gemma DSL. -#}
+                        {%- set argstr = function['arguments'] | trim -%}
+                        {%- if argstr[:1] == '{' and argstr[-1:] == '}' -%}
+                            {{- argstr[1:-1] -}}
+                        {%- else -%}
+                            {{- function['arguments'] -}}
+                        {%- endif -%}
+                    {%- endif -%}
+                    {{- '}<tool_call|>' -}}
+                {%- endfor -%}
+                {%- set ns.prev_message_type = 'tool_call' -%}
+            {%- endif -%}
+
+            {%- set ns_tr_out = namespace(flag=false) -%}
+            {%- if message.get('tool_responses') -%}
+                {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}
+                {%- for tool_response in message.get('tool_responses') -%}
+                    {{- format_tool_response_block(tool_response['name'] | default('unknown', true), tool_response['response']) -}}
+                    {%- set ns_tr_out.flag = true -%}
+                    {%- set ns.prev_message_type = 'tool_response' -%}
+                {%- endfor -%}
+            {%- elif message.get('tool_calls') -%}
+                {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}
+                {%- set ns_tool_scan = namespace(stopped=false) -%}
+                {%- for k in range(loop.index0 + 1, loop_messages | length) -%}
+                    {%- if ns_tool_scan.stopped -%}
+                    {%- elif loop_messages[k]['role'] != 'tool' -%}
+                        {%- set ns_tool_scan.stopped = true -%}
+                    {%- else -%}
+                        {%- set follow = loop_messages[k] -%}
+                        {#- Resolve tool_call_id to function name -#}
+                        {%- set ns_tname = namespace(name=follow.get('name') or 'unknown') -%}
+                        {%- for tc in message.get('tool_calls') -%}
+                            {%- if tc.get('id') == follow.get('tool_call_id') -%}
+                                {%- set ns_tname.name = tc['function']['name'] -%}
+                            {%- endif -%}
+                        {%- endfor -%}
+                        {#- Handle content as string or content-parts array -#}
+                        {%- set tool_body = follow.get('content') -%}
+                        {%- if tool_body is string -%}
+                            {{- format_tool_response_block(ns_tname.name, tool_body) -}}
+                        {%- elif tool_body is sequence and tool_body is not string -%}
+                            {%- set ns_txt = namespace(s='') -%}
+                            {%- for part in tool_body -%}
+                                {%- if part.get('type') == 'text' -%}
+                                    {%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%}
+                                {%- endif -%}
+                            {%- endfor -%}
+                            {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
+                            {%- for part in tool_body -%}
+                                {%- if part.get('type') in ['image', 'image_url'] -%}
+                                    {{- '<|image|>' -}}
+                                {%- elif part.get('type') in ['audio', 'input_audio'] -%}
+                                    {{- '<|audio|>' -}}
+                                {%- elif part.get('type') == 'video' -%}
+                                    {{- '<|video|>' -}}
+                                {%- endif -%}
+                            {%- endfor -%}
+                        {%- else -%}
+                            {{- format_tool_response_block(ns_tname.name, tool_body) -}}
+                        {%- endif -%}
+                        {%- set ns_tr_out.flag = true -%}
+                        {%- set ns.prev_message_type = 'tool_response' -%}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- endif -%}
+
+            {%- set captured_content -%}
+            {%- if message.get('content') is string -%}
+                {%- if role == 'model' -%}
+                    {{- strip_thinking(message['content']) -}}
+                {%- else -%}
+                    {{- message['content'] | trim -}}
+                {%- endif -%}
+            {%- elif message.get('content') is sequence -%}
+                {%- for item in message['content'] -%}
+                    {%- if item.get('type') == 'text' -%}
+                        {%- if role == 'model' -%}
+                            {{- strip_thinking(item['text']) -}}
+                        {%- else -%}
+                            {{- item['text'] | trim -}}
+                        {%- endif -%}
+                    {%- elif item.get('type') in ['image', 'image_url'] -%}
+                        {{- '<|image|>' -}}
+                    {%- elif item.get('type') in ['audio', 'input_audio'] -%}
+                        {{- '<|audio|>' -}}
+                    {%- elif item.get('type') == 'video' -%}
+                        {{- '<|video|>' -}}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- endif -%}
+            {%- endset -%}
+
+            {{- captured_content -}}
+            {%- set has_content = captured_content | trim | length > 0 -%}
+
+        {#- Forward-scan: find next non-tool message role for continuation detection -#}
+        {%- set next_nt = namespace(role=None, found=false) -%}
+        {%- for j in range(loop.index0 + 1, loop_messages | length) -%}
+            {%- if not next_nt.found -%}
+                {%- if loop_messages[j]['role'] != 'tool' -%}
+                    {%- set next_nt.role = loop_messages[j]['role'] -%}
+                    {%- set next_nt.found = true -%}
+                {%- endif -%}
+            {%- endif -%}
+        {%- endfor -%}
+
+        {%- set continues_into_next = (
+            role == 'model'
+            and next_nt.role == 'assistant'
+            and (not message.get('tool_calls') or ns_tr_out.flag)
+        ) -%}
+
+        {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
+            {{- '<|tool_response>' -}}
+        {%- elif continues_into_next -%}
+        {%- elif not (ns_tr_out.flag and not has_content and not next_nt.found) -%}
+            {{- '<turn|>\n' -}}
+        {%- endif -%}
+
+    {#- Track previous non-tool role for next iteration (avoids O(n) backward scan) -#}
+    {%- set ns.prev_non_tool_role = message['role'] -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}
+        {{- '<|turn>model\n' -}}
+    {%- elif ns.prev_message_type == 'tool_response' and enable_thinking -%}
+        {{- '<|channel>thought\n' -}}
+    {%- endif -%}
+{%- endif -%}

config.json

@@ -0,0 +1,199 @@
+{
+  "architectures": [
+    "Gemma4ForConditionalGeneration"
+  ],
+  "audio_config": {
+    "_name_or_path": "",
+    "architectures": null,
+    "attention_chunk_size": 12,
+    "attention_context_left": 13,
+    "attention_context_right": 0,
+    "attention_invalid_logits_value": -1000000000.0,
+    "attention_logit_cap": 50.0,
+    "chunk_size_feed_forward": 0,
+    "conv_kernel_size": 5,
+    "dtype": "bfloat16",
+    "gradient_clipping": 10000000000.0,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "model_type": "gemma4_audio",
+    "num_attention_heads": 8,
+    "num_hidden_layers": 12,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_proj_dims": 1536,
+    "problem_type": null,
+    "residual_weight": 0.5,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "subsampling_conv_channels": [
+      128,
+      32
+    ],
+    "use_clipped_linears": true
+  },
+  "audio_token_id": 258881,
+  "boa_token_id": 256000,
+  "boi_token_id": 255999,
+  "dtype": "bfloat16",
+  "eoa_token_id": 258883,
+  "eoa_token_index": 258883,
+  "eoi_token_id": 258882,
+  "eos_token_id": 106,
+  "image_token_id": 258880,
+  "initializer_range": 0.02,
+  "model_name": "unsloth/gemma-4-e4b-it",
+  "model_type": "gemma4",
+  "pad_token_id": 0,
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attention_k_eq_v": false,
+    "bos_token_id": 2,
+    "dtype": "bfloat16",
+    "enable_moe_block": false,
+    "eos_token_id": 1,
+    "expert_intermediate_size": null,
+    "final_logit_softcapping": 30.0,
+    "global_head_dim": 512,
+    "head_dim": 256,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 2560,
+    "hidden_size_per_layer_input": 256,
+    "initializer_range": 0.02,
+    "intermediate_size": 10240,
+    "layer_types": [
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 131072,
+    "model_type": "gemma4_text",
+    "moe_intermediate_size": null,
+    "num_attention_heads": 8,
+    "num_experts": null,
+    "num_global_key_value_heads": null,
+    "num_hidden_layers": 42,
+    "num_key_value_heads": 2,
+    "num_kv_shared_layers": 18,
+    "pad_token_id": 0,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "full_attention": {
+        "partial_rotary_factor": 0.25,
+        "rope_theta": 1000000.0,
+        "rope_type": "proportional"
+      },
+      "sliding_attention": {
+        "rope_theta": 10000.0,
+        "rope_type": "default"
+      }
+    },
+    "sliding_window": 512,
+    "tie_word_embeddings": true,
+    "top_k_experts": null,
+    "use_bidirectional_attention": null,
+    "use_cache": false,
+    "use_double_wide_mlp": false,
+    "vocab_size": 262144,
+    "vocab_size_per_layer_input": 262144
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.8.22",
+  "video_token_id": 258884,
+  "vision_config": {
+    "_name_or_path": "",
+    "architectures": null,
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "chunk_size_feed_forward": 0,
+    "default_output_length": 280,
+    "dtype": "bfloat16",
+    "global_head_dim": 64,
+    "head_dim": 64,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "max_position_embeddings": 131072,
+    "model_type": "gemma4_vision",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 16,
+    "num_key_value_heads": 12,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "position_embedding_size": 10240,
+    "problem_type": null,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "rope_theta": 100.0,
+      "rope_type": "default"
+    },
+    "standardize": false,
+    "use_clipped_linears": true
+  },
+  "vision_soft_tokens_per_image": 280
+}

eval_report.json

@@ -0,0 +1,78 @@
+{
+  "model": "data/models/v1-merged",
+  "eval_file": "data/sft/eval.jsonl",
+  "n_errorful": 1088,
+  "n_clean": 1088,
+  "json_parse_rate": 100.0,
+  "exact_match": 713,
+  "exact_match_pct": 65.53308823529412,
+  "tp": 753,
+  "fp": 120,
+  "fn": 416,
+  "precision": 86.25429553264605,
+  "recall": 64.41402908468777,
+  "f05": 80.77665736966317,
+  "unchanged_error": 255,
+  "changed_wrong": 120,
+  "per_category_recall": {
+    "morphology": {
+      "tp": 225,
+      "total": 292,
+      "recall": 77.05479452054794
+    },
+    "spelling": {
+      "tp": 154,
+      "total": 170,
+      "recall": 90.58823529411765
+    },
+    "punctuation": {
+      "tp": 188,
+      "total": 258,
+      "recall": 72.86821705426357
+    },
+    "capitalization": {
+      "tp": 33,
+      "total": 33,
+      "recall": 100.0
+    },
+    "word_level": {
+      "tp": 36,
+      "total": 36,
+      "recall": 100.0
+    },
+    "zalantza": {
+      "tp": 82,
+      "total": 89,
+      "recall": 92.13483146067416
+    },
+    "proper_noun": {
+      "tp": 57,
+      "total": 83,
+      "recall": 68.67469879518072
+    },
+    "calque": {
+      "tp": 13,
+      "total": 76,
+      "recall": 17.105263157894736
+    },
+    "terminology": {
+      "tp": 45,
+      "total": 51,
+      "recall": 88.23529411764706
+    }
+  },
+  "per_nature_recall": {
+    "error": {
+      "tp": 788,
+      "total": 1037,
+      "recall": 75.98842815814851
+    },
+    "suggestion": {
+      "tp": 45,
+      "total": 51,
+      "recall": 88.23529411764706
+    }
+  },
+  "clean_fp": 94,
+  "clean_fp_pct": 8.63970588235294
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 2,
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106,
+    50
+  ],
+  "pad_token_id": 0,
+  "temperature": 1.0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d73cfb848ffd9e01b8de1598fbdb7c42e8ed2593c7ae99780d923780f6630d8
+size 15993973020

processor_config.json

@@ -0,0 +1,75 @@
+{
+  "audio_ms_per_token": 40,
+  "audio_seq_length": 750,
+  "feature_extractor": {
+    "dither": 0.0,
+    "feature_extractor_type": "Gemma4AudioFeatureExtractor",
+    "feature_size": 128,
+    "fft_length": 512,
+    "fft_overdrive": false,
+    "frame_length": 320,
+    "hop_length": 160,
+    "input_scale_factor": 1.0,
+    "max_frequency": 8000.0,
+    "mel_floor": 0.001,
+    "min_frequency": 0.0,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "per_bin_mean": null,
+    "per_bin_stddev": null,
+    "preemphasis": 0.0,
+    "preemphasis_htk_flavor": true,
+    "return_attention_mask": true,
+    "sampling_rate": 16000
+  },
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": false,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.0,
+      0.0,
+      0.0
+    ],
+    "image_processor_type": "Gemma4ImageProcessor",
+    "image_seq_length": 280,
+    "image_std": [
+      1.0,
+      1.0,
+      1.0
+    ],
+    "max_soft_tokens": 280,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098
+  },
+  "image_seq_length": 280,
+  "processor_class": "Gemma4Processor",
+  "video_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "image_mean": [
+      0.0,
+      0.0,
+      0.0
+    ],
+    "image_std": [
+      1.0,
+      1.0,
+      1.0
+    ],
+    "max_soft_tokens": 70,
+    "num_frames": 32,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "video_processor_type": "Gemma4VideoProcessor"
+  }
+}

system_prompt.txt

@@ -0,0 +1 @@
+Zuzendu emandako euskara testuko akatsak, aldaketa minimalak eginez, eta azaldu edizio bakoitza. Jarraitu honako arau hau: «EBE aditz-taulak eta eranskina: aditz komunztadura, deklinabide-atxiki eta pertsona-izenordainak EBEk ezarritako paradien arabera erabili». Eman emaitza JSON gisa: {"target": "<testu zuzendua>", "edits": [{"src_content": "<jatorrizkoa>", "tgt_content": "<zuzena>", "category": "...", "error_type": "...", "error_severity": 1, "nature": "error", "ref": "EBE ...", "error_description": "..."}]}.

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
+size 32169626

tokenizer_config.json

@@ -0,0 +1,289 @@
+{
+  "audio_token": "<|audio|>",
+  "backend": "tokenizers",
+  "boa_token": "<|audio>",
+  "boi_token": "<|image>",
+  "bos_token": "<bos>",
+  "eoa_token": "<audio|>",
+  "eoc_token": "<channel|>",
+  "eoi_token": "<image|>",
+  "eos_token": "<turn|>",
+  "eot_token": "<turn|>",
+  "escape_token": "<|\"|>",
+  "etc_token": "<tool_call|>",
+  "etd_token": "<tool|>",
+  "etr_token": "<tool_response|>",
+  "extra_special_tokens": [
+    "<|video|>"
+  ],
+  "image_token": "<|image|>",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 131072,
+  "model_specific_special_tokens": {
+    "audio_token": "<|audio|>",
+    "boa_token": "<|audio>",
+    "boi_token": "<|image>",
+    "eoa_token": "<audio|>",
+    "eoc_token": "<channel|>",
+    "eoi_token": "<image|>",
+    "eot_token": "<turn|>",
+    "escape_token": "<|\"|>",
+    "etc_token": "<tool_call|>",
+    "etd_token": "<tool|>",
+    "etr_token": "<tool_response|>",
+    "image_token": "<|image|>",
+    "soc_token": "<|channel>",
+    "sot_token": "<|turn>",
+    "stc_token": "<|tool_call>",
+    "std_token": "<|tool>",
+    "str_token": "<|tool_response>",
+    "think_token": "<|think|>"
+  },
+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "processor_class": "Gemma4Processor",
+  "response_schema": {
+    "properties": {
+      "content": {
+        "type": "string"
+      },
+      "role": {
+        "const": "assistant"
+      },
+      "thinking": {
+        "type": "string"
+      },
+      "tool_calls": {
+        "items": {
+          "properties": {
+            "function": {
+              "properties": {
+                "arguments": {
+                  "additionalProperties": {},
+                  "type": "object",
+                  "x-parser": "gemma4-tool-call"
+                },
+                "name": {
+                  "type": "string"
+                }
+              },
+              "type": "object",
+              "x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
+            },
+            "type": {
+              "const": "function"
+            }
+          },
+          "type": "object"
+        },
+        "type": "array",
+        "x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>"
+      }
+    },
+    "type": "object",
+    "x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
+  },
+  "soc_token": "<|channel>",
+  "sot_token": "<|turn>",
+  "stc_token": "<|tool_call>",
+  "std_token": "<|tool>",
+  "str_token": "<|tool_response>",
+  "think_token": "<|think|>",
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "1": {
+      "content": "<eos>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
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