initial release: clario v2 LoRA + model card

.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 +1,249 @@
+---
+library_name: peft
+base_model: unsloth/gemma-4-e4b-it
+license: cc-by-4.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- medical
+- clinical-nlp
+- ner
+- hpo
+- rare-disease
+- lora
+- peft
+- gemma
+- entity-extraction
+- symptom-extraction
+datasets:
+- m0rtyddd/clario-synthetic-diary
+model-index:
+- name: clario-gemma4-e4b-lora-v2
+  results:
+  - task:
+      type: token-classification
+      name: Symptom entity extraction
+    dataset:
+      name: clario-synthetic-diary (val split, held-out by disease)
+      type: m0rtyddd/clario-synthetic-diary
+    metrics:
+    - type: f1
+      name: Name F1 (synonym-aware)
+      value: 0.524
+    - type: f1
+      name: HPO ID F1 (via name to HPO lookup)
+      value: 0.524
+    - type: accuracy
+      name: JSON schema correctness
+      value: 1.0
+    - type: accuracy
+      name: Entity type accuracy
+      value: 1.0
+---
+
+# Clario · Gemma 4 E4B · Symptom-Diary LoRA (v2)
+
+LoRA adapter on `unsloth/gemma-4-e4b-it` that converts colloquial patient
+diary text into a structured JSON list of medical entities with canonical
+names suitable for HPO (Human Phenotype Ontology) lookup. Trained on
+411 distilled `(diary, target_json)` pairs derived from Orphanet rare-disease
+phenotypes and HPO synonyms.
+
+The adapter is one stage of a deterministic pipeline:
+
+```
+diary text  →  [LoRA-adapted Gemma 4 E4B, 4-bit]  →  JSON entities
+            →  deterministic HPO synonym lookup  →  HPO IDs + canonical names
+```
+
+The model is **not** asked to memorise the ~17k HPO IDs from 411 examples.
+Its job is to extract and canonicalise symptom mentions; IDs are resolved
+afterwards from a versioned ontology (see *Limitations* §1).
+
+## Headline results
+
+Held-out 68-example validation set (split by disease — 651 Orphanet
+disorders never appear in training). Baseline: the same
+`unsloth/gemma-4-e4b-it` in 4-bit *without* the adapter, apples-to-apples,
+same system prompt.
+
+| Metric | Baseline | Fine-tuned | Δ |
+|---|--:|--:|--:|
+| JSON schema correctness | 0%¹ | **100%** | +100 pp |
+| Entity type-field accuracy | 89.1% | **100%** | +10.9 pp |
+| Name F1 (synonym-aware) | 0.209 | **0.524** | **+151%** |
+| HPO ID F1 (via name→HPO lookup) | 0.349 | **0.524** | +50% |
+| Avg entities per example (gold = 2.93) | 5.37 (over-extract) | **2.91** (calibrated) | — |
+
+¹ Vanilla Gemma 4 emits its own ad-hoc shape
+(`{symptoms, triggers, body_parts, medications, lab_values}`); the baseline
+row above is computed *after* normalising those outputs to the requested
+schema. Without normalisation, every baseline score would be 0.
+
+## Quick start
+
+```python
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+
+BASE = "unsloth/gemma-4-e4b-it"
+ADAPTER = "m0rtyddd/clario-gemma4-e4b-lora-v2"
+
+bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype="bfloat16")
+tok = AutoTokenizer.from_pretrained(ADAPTER)
+base = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb, device_map="auto")
+model = PeftModel.from_pretrained(base, ADAPTER)
+model.eval()
+
+SYSTEM = (
+    "Extract medical entities from the diary. Return strict JSON: "
+    '{"entities":[{"name_colloquial":"…","name_canonical":"…",'
+    '"hpo_id":"HP:…","type":"symptom|lab_marker|med|trigger|behavior"}]}. '
+    "Use canonical HPO names where possible. Output JSON only."
+)
+diary = (
+    "My eyes have been gritty, like there's sand in them. Mouth's been so "
+    "dry I can't swallow toast without water. Fingers ache when I type for long."
+)
+
+prompt = tok.apply_chat_template(
+    [{"role": "system", "content": SYSTEM}, {"role": "user", "content": diary}],
+    tokenize=False, add_generation_prompt=True,
+)
+ids = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**ids, max_new_tokens=512, do_sample=False)
+response = tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True)
+print(json.loads(response))
+# {"entities": [
+#   {"name_colloquial": "gritty eyes",  "name_canonical": "Keratoconjunctivitis sicca", ...},
+#   {"name_colloquial": "mouth so dry", "name_canonical": "Xerostomia", ...},
+#   {"name_colloquial": "fingers ache", "name_canonical": "Arthralgia",  ...},
+# ]}
+```
+
+**Production note.** Discard the model's `hpo_id` field and resolve from
+`name_canonical` via a synonym index built from HPO `hp.obo`
+(42k normalised name → HP:ID entries). See *Limitations* §1.
+
+## Pipeline integration
+
+Reference integration (FastAPI sidecar + Clario backend) is open-source:
+
+- **Sidecar** (this adapter + HPO post-lookup + few-shot SLE demos):
+  `scripts/clario_extractor_service.py`.
+- **Consumer:** `backend/diary/extraction.py::process_one` calls the
+  sidecar when `CLARIO_EXTRACTOR_URL` is set, otherwise falls back to
+  vanilla Gemma via Ollama.
+
+## Training
+
+| | |
+|---|---|
+| Base | `unsloth/gemma-4-e4b-it` (4-bit NF4, BF16 compute) |
+| Adapter | LoRA, r=16, α=32, dropout=0.05 |
+| Target modules | `q/k/v/o/gate/up/down_proj` on all language-model layers |
+| Optimiser | `adamw_8bit` |
+| Learning rate | 5e-5 |
+| Epochs | 1 (on top of a resumed `checkpoint-50` from the v1 run) |
+| Max sequence length | 1536 |
+| Total steps | 22 SGD steps over 343 train examples |
+| Train loss (final) | 0.478 |
+| Hardware | single RTX 5060 Ti 16 GB (Blackwell sm_120) |
+| Wall time | ~22 min |
+
+The first training run hung at step 50/66 due to a known interaction
+between `paged_adamw_8bit` and Windows NVIDIA driver 596.36. The resumed
+run with `adamw_8bit` completed cleanly.
+
+Training data is published as
+[`m0rtyddd/clario-synthetic-diary`](https://huggingface.co/datasets/m0rtyddd/clario-synthetic-diary).
+
+## Intended use
+
+- Extracting symptom, lab-marker, medication, trigger, and behaviour
+  entities from short English-language patient diary entries.
+- As the *extraction* stage of a longer pipeline that includes
+  deterministic HPO-ID resolution and downstream graph / hypothesis-engine
+  logic.
+- Research and educational use around rare-disease symptom canonicalisation.
+
+## Out of scope
+
+- **Direct clinical decision-making.** This is an extraction model, not a
+  diagnostic one. Outputs require human-in-the-loop review.
+- **Non-English diaries.** All training data is English (see §5 below).
+- **HPO ID generation.** Use the deterministic lookup; do not consume
+  `entities[].hpo_id` from the model (see §1 below).
+- **Rationale / explanation generation** for downstream hypothesis ranking
+  — this LoRA does not improve that path.
+
+## Limitations
+
+**1. The model does not faithfully generate HPO IDs.** 411 training pairs
+cover ~1.5k unique HPO terms out of the ontology's ~17k. On free-form
+generation the model hallucinates sequential dummies
+(`HP:0001211, HP:0001212, HP:0001213, …`). The +50% relative HPO F1 over
+baseline measures how the better *name* extraction improves end-to-end ID
+resolution **once a deterministic lookup is applied**. Consumers must
+discard the model's `hpo_id` field.
+
+**2. Eval is synthetic, held-out by disease, but not clinical.** Both
+train and validation come from the same `gpt-oss:20b` teacher pipeline.
+651 Orphanet disorders never appear in train, so the split is non-trivially
+novel — but the val set is not a clinical golden. A manual 200-pair
+golden set is the next measurement gate and was not completed within the
+hackathon window.
+
+**3. Two few-shot demonstrations stack on top of the LoRA for the SLE
+pattern.** The LoRA alone correctly extracts the MCAS-style (Tom) and
+Hashimoto-style (Anna) personas with no prompt scaffolding. For SLE
+specifically, the upstream sidecar prepends two demonstrations to the
+chat history (showing "reddened band across cheekbones → Malar rash" and
+"burning skin where sun hit → Photosensitivity", with surface forms
+*different* from any test diary) to push the model toward those two
+textbook SLE phenotypes. Both contributions stack.
+
+**4. Rationale generation is unchanged.** The adapter is scoped to
+extraction. Downstream hypothesis rationales still run on vanilla
+Gemma 4 weights with templated phrasing.
+
+**5. English only.** No Russian, Italian, or other-language training data.
+Frontend i18n slots exist upstream, but the extractor would need a
+translate-pass or additional fine-tuning for non-English diaries.
+
+## Licence and attribution
+
+This adapter is released under **CC-BY-4.0**, propagating the licence of
+its training-data sources:
+
+- **HPO** (Human Phenotype Ontology) phenotype names and synonyms —
+  CC-BY-4.0.
+  Köhler S. *et al.* (2021) *The Human Phenotype Ontology in 2021.*
+  Nucleic Acids Research, 49(D1):D1207–D1217. <https://hpo.jax.org/>
+- **Orphanet** rare-disease ↔ phenotype annotations
+  (`en_product4.xml`, 2026-05-13 snapshot) — free for academic and
+  commercial use with attribution.
+  Orphadata: *Free access products on rare diseases and orphan drugs.*
+  INSERM 1978. <https://www.orphadata.com/>
+- **Base model:** `unsloth/gemma-4-e4b-it`, used under the Gemma Terms of
+  Use.
+- **Teacher:** `gpt-oss:20b` (Apache-2.0).
+
+Cite as:
+
+```bibtex
+@misc{okulov2026clario_lora_v2,
+  title  = {{Clario} {Gemma 4 E4B} Symptom-Diary {LoRA} (v2)},
+  author = {Okulov, Maksim},
+  year   = {2026},
+  howpublished = {\url{https://huggingface.co/m0rtyddd/clario-gemma4-e4b-lora-v2}}
+}
+```
+
+## Frameworks
+
+`peft 0.19.1` · `trl 1.4.0` · `transformers 5.8.0.dev0` ·
+`torch 2.11.0.dev20260108+cu128` · `datasets 4.8.5` · `tokenizers 0.22.2` ·
+`unsloth` (QLoRA path) · `bitsandbytes` (4-bit NF4)

adapter_config.json

@@ -0,0 +1,40 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/gemma-4-e4b-it",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "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": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": "model\\.language_model\\..*\\.(q_proj|k_proj|v_proj|o_proj|gate_proj|up_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

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cce987cb1a3a3eb56ef9407d8c52ae0434dae946ccc4d472694b7dfcbd8e78c3
+size 69839888

chat_template.jinja

@@ -0,0 +1,351 @@
+{%- 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 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 -%}
+
+{%- set ns = namespace(prev_message_type=None) -%}
+{%- set loop_messages = messages -%}
+{{- bos_token -}}
+{#- Handle System/Tool Definitions Block -#}
+{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}
+    {{- '<|turn>system\n' -}}
+    {#- Inject Thinking token at the very top of the FIRST system turn -#}
+    {%- if enable_thinking is defined and enable_thinking -%}
+        {{- '<|think|>\n' -}}
+        {%- set ns.prev_message_type = 'think' -%}
+    {%- endif -%}
+    {%- if 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: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}
+    {%- set prev_nt = namespace(role=None, found=false) -%}
+    {%- if loop.index0 > 0 -%}
+        {%- for j in range(loop.index0 - 1, -1, -1) -%}
+            {%- if not prev_nt.found -%}
+                {%- if loop_messages[j]['role'] != 'tool' -%}
+                    {%- set prev_nt.role = loop_messages[j]['role'] -%}
+                    {%- set prev_nt.found = true -%}
+                {%- endif -%}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- endif -%}
+    {%- set continue_same_model_turn = (role == 'model' and prev_nt.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') -%}
+    {%- if thinking_text and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%}
+        {{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
+    {%- endif -%}
+
+            {%- if message['tool_calls'] -%}
+                {%- for tool_call in message['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 string -%}
+                        {{- function['arguments'] -}}
+                    {%- 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['tool_responses'] -%}
+                    {{- format_tool_response_block(tool_response['name'] | default('unknown'), 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') | default('unknown')) -%}
+                        {%- for tc in message['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) -}}
+                        {%- 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['content'] is string -%}
+                {%- if role == 'model' -%}
+                    {{- strip_thinking(message['content']) -}}
+                {%- else -%}
+                    {{- message['content'] | trim -}}
+                {%- endif -%}
+            {%- elif message['content'] is sequence -%}
+                {%- for item in message['content'] -%}
+                    {%- if item['type'] == 'text' -%}
+                        {%- if role == 'model' -%}
+                            {{- strip_thinking(item['text']) -}}
+                        {%- else -%}
+                            {{- item['text'] | trim -}}
+                        {%- endif -%}
+                    {%- elif item['type'] == 'image' -%}
+                        {{- '<|image|>' -}}
+                        {%- set ns.prev_message_type = 'image' -%}
+                    {%- elif item['type'] == 'audio' -%}
+                        {{- '<|audio|>' -}}
+                        {%- set ns.prev_message_type = 'audio' -%}
+                    {%- elif item['type'] == 'video' -%}
+                        {{- '<|video|>' -}}
+                        {%- set ns.prev_message_type = 'video' -%}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- endif -%}
+            {%- endset -%}
+
+            {{- captured_content -}}
+            {%- set has_content = captured_content | trim | length > 0 -%}
+
+        {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
+            {{- '<|tool_response>' -}}
+        {%- elif not (ns_tr_out.flag and not has_content) -%}
+            {{- '<turn|>\n' -}}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}
+
+{%- if add_generation_prompt -%}
+    {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}
+        {{- '<|turn>model\n' -}}
+    {%- endif -%}
+{%- endif -%}

tokenizer.json

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

tokenizer_config.json

@@ -0,0 +1,96 @@
+{
+  "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,
+  "local_files_only": 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>"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44adcec10a3f291931b5b99ee50b90fe457b5b795f002103927b4a6ad411fb41
+size 5777