Image-Text-to-Text
Transformers
Safetensors
gemma4
gemma
gemma-4
clinical
medical
clinical-reasoning
icu
clin-react
conversational
Instructions to use iheallab/Clin-REACT-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iheallab/Clin-REACT-31B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="iheallab/Clin-REACT-31B") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("iheallab/Clin-REACT-31B") model = AutoModelForMultimodalLM.from_pretrained("iheallab/Clin-REACT-31B", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use iheallab/Clin-REACT-31B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "iheallab/Clin-REACT-31B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "iheallab/Clin-REACT-31B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/iheallab/Clin-REACT-31B
- SGLang
How to use iheallab/Clin-REACT-31B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "iheallab/Clin-REACT-31B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "iheallab/Clin-REACT-31B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "iheallab/Clin-REACT-31B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "iheallab/Clin-REACT-31B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use iheallab/Clin-REACT-31B with Docker Model Runner:
docker model run hf.co/iheallab/Clin-REACT-31B
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +114 -0
- chat_template.jinja +266 -0
- config.json +177 -0
- generation_config.json +14 -0
- model-00001-of-00013.safetensors +3 -0
- model-00002-of-00013.safetensors +3 -0
- model-00003-of-00013.safetensors +3 -0
- model-00004-of-00013.safetensors +3 -0
- model-00005-of-00013.safetensors +3 -0
- model-00006-of-00013.safetensors +3 -0
- model-00007-of-00013.safetensors +3 -0
- model-00008-of-00013.safetensors +3 -0
- model-00009-of-00013.safetensors +3 -0
- model-00010-of-00013.safetensors +3 -0
- model-00011-of-00013.safetensors +3 -0
- model-00012-of-00013.safetensors +3 -0
- model-00013-of-00013.safetensors +3 -0
- model.safetensors.index.json +0 -0
- tokenizer.json +3 -0
- tokenizer_config.json +99 -0
.gitattributes
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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---
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---
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base_model: google/gemma-4-31B-it
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base_model_relation: finetune
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library_name: transformers
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pipeline_tag: image-text-to-text
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license: apache-2.0
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tags:
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- gemma
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- gemma-4
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- clinical
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- medical
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- clinical-reasoning
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- icu
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- clin-react
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---
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# Clin-REACT-31B
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> **Model name in the manuscript:** Clin-REACT 31B
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> **Base model:** `google/gemma-4-31B-it`
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> **Release format:** merged full-parameter checkpoint
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Clin-REACT is a family of large language models fine-tuned for clinical reasoning using the ICU-REACT framework. The models are designed to reason over clinically relevant information and produce responses for tasks spanning ICU decision-making and broader clinical-reasoning benchmarks.
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This repository contains the **merged, self-contained checkpoint**. The LoRA adapter used during supervised fine-tuning has been merged into the corresponding base-model weights for distribution and inference.
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The underlying Gemma 4 architecture is multimodal. The Clin-REACT fine-tuning and benchmark results reported in this model card are **text-based**; inherited multimodal capabilities were not evaluated as part of these results.
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## Benchmark results
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Clin-REACT 31B is compared with its Gemma 4 31B IT backbone and selected strong open models, including substantially larger baselines.
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Scores below are the **mean primary benchmark scores**, reported as percentages. Higher is better. The macro average is the unweighted mean of the five benchmark primary scores. **Bold** indicates the best result within the comparison set shown for each benchmark.
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The benchmarks contain 71 ICU-REACT cases, 174 SCT-Bench cases, 72 ER-Reason cases, 1,254 MedRBench cases, and 934 VivaBench cases. Because the benchmarks use different task formulations and primary scoring procedures, individual benchmark scores should primarily be interpreted within each benchmark.
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| Model | ICU-REACT (n=71) | SCT-Bench (n=174) | ER-Reason (n=72) | MedRBench (n=1254) | VivaBench (n=934) | Macro avg. |
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|---|---:|---:|---:|---:|---:|---:|
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| Clin-REACT 31B | **44.8** | 75.5 | **51.4** | 46.9 | **33.5** | **50.4** |
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| Gemma 4 31B IT | 40.6 | **77.6** | 47.4 | 44.0 | 31.6 | 48.3 |
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| Baichuan M2 32B | 36.9 | 63.0 | 46.2 | 38.4 | 26.9 | 42.3 |
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| Llama 3.3 70B Instruct | 30.9 | 59.9 | 45.2 | 45.1 | 26.7 | 41.6 |
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| GPT-OSS-120B | 40.1 | 74.3 | 48.3 | **47.0** | 32.5 | 48.5 |
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## Installation
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```bash
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pip install -U torch transformers accelerate
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```
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## Load and use the model
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Gemma 4 uses a multimodal processor/model interface even for text-only prompts.
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```python
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import torch
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from transformers import AutoProcessor, AutoModelForMultimodalLM
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MODEL_ID = "macontreras98/Clin-REACT-31B"
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processor = AutoProcessor.from_pretrained(MODEL_ID)
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model = AutoModelForMultimodalLM.from_pretrained(
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MODEL_ID,
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dtype="auto",
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a helpful clinical reasoning assistant."},
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{"role": "user", "content": "Enter your clinical question or case here."},
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]
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inputs = processor.apply_chat_template(
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messages,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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add_generation_prompt=True,
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).to(model.device)
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input_len = inputs["input_ids"].shape[-1]
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with torch.inference_mode():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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do_sample=False,
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)
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response = processor.decode(
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outputs[0][input_len:],
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skip_special_tokens=False,
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)
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# Gemma 4 provides a helper for parsing the generated response.
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parsed = processor.parse_response(
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response,
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prefix=inputs["input_ids"],
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)
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print(parsed)
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```
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For exact benchmark reproduction, use the same prompting, processor/chat template, preprocessing, thinking-mode configuration, and decoding settings used in the ICU-REACT evaluation pipeline.
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## Intended use and limitations
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Clin-REACT is released for research on clinical reasoning and medical AI. It is **not a medical device** and should not be used as a substitute for professional clinical judgment, diagnosis, treatment decisions, or other autonomous patient-care decisions.
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Benchmark performance does not establish clinical safety, prospective effectiveness, or suitability for deployment. Users are responsible for evaluating the model for their own setting and for complying with applicable privacy, security, institutional, and regulatory requirements.
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## License
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The base model `google/gemma-4-31B-it` is licensed under the **Apache License 2.0**. This repository distributes a modified derivative checkpoint. See the `LICENSE` file and retain any applicable upstream copyright and attribution notices.
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## Citation
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If you use Clin-REACT in your research, please cite the associated ICU-REACT / Clin-REACT manuscript. A finalized BibTeX citation can be added here upon publication.
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chat_template.jinja
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{%- macro format_parameters(properties, required) -%}
|
| 2 |
+
{%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
|
| 3 |
+
{%- set ns = namespace(found_first=false) -%}
|
| 4 |
+
{%- for key, value in properties | dictsort -%}
|
| 5 |
+
{%- set add_comma = false -%}
|
| 6 |
+
{%- if key not in standard_keys -%}
|
| 7 |
+
{%- if ns.found_first %},{% endif -%}
|
| 8 |
+
{%- set ns.found_first = true -%}
|
| 9 |
+
{{ key }}:{
|
| 10 |
+
{%- if value['description'] -%}
|
| 11 |
+
description:<|"|>{{ value['description'] }}<|"|>
|
| 12 |
+
{%- set add_comma = true -%}
|
| 13 |
+
{%- endif -%}
|
| 14 |
+
{%- if value['nullable'] %}
|
| 15 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 16 |
+
nullable:true
|
| 17 |
+
{%- endif -%}
|
| 18 |
+
{%- if value['type'] | upper == 'STRING' -%}
|
| 19 |
+
{%- if value['enum'] -%}
|
| 20 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 21 |
+
enum:{{ format_argument(value['enum']) }}
|
| 22 |
+
{%- endif -%}
|
| 23 |
+
{%- elif value['type'] | upper == 'OBJECT' -%}
|
| 24 |
+
,properties:{
|
| 25 |
+
{%- if value['properties'] is defined and value['properties'] is mapping -%}
|
| 26 |
+
{{- format_parameters(value['properties'], value['required'] | default([])) -}}
|
| 27 |
+
{%- elif value is mapping -%}
|
| 28 |
+
{{- format_parameters(value, value['required'] | default([])) -}}
|
| 29 |
+
{%- endif -%}
|
| 30 |
+
}
|
| 31 |
+
{%- if value['required'] -%}
|
| 32 |
+
,required:[
|
| 33 |
+
{%- for item in value['required'] | default([]) -%}
|
| 34 |
+
<|"|>{{- item -}}<|"|>
|
| 35 |
+
{%- if not loop.last %},{% endif -%}
|
| 36 |
+
{%- endfor -%}
|
| 37 |
+
]
|
| 38 |
+
{%- endif -%}
|
| 39 |
+
{%- elif value['type'] | upper == 'ARRAY' -%}
|
| 40 |
+
{%- if value['items'] is mapping and value['items'] -%}
|
| 41 |
+
,items:{
|
| 42 |
+
{%- set ns_items = namespace(found_first=false) -%}
|
| 43 |
+
{%- for item_key, item_value in value['items'] | dictsort -%}
|
| 44 |
+
{%- if item_value is not none -%}
|
| 45 |
+
{%- if ns_items.found_first %},{% endif -%}
|
| 46 |
+
{%- set ns_items.found_first = true -%}
|
| 47 |
+
{%- if item_key == 'properties' -%}
|
| 48 |
+
properties:{
|
| 49 |
+
{%- if item_value is mapping -%}
|
| 50 |
+
{{- format_parameters(item_value, value['items']['required'] | default([])) -}}
|
| 51 |
+
{%- endif -%}
|
| 52 |
+
}
|
| 53 |
+
{%- elif item_key == 'required' -%}
|
| 54 |
+
required:[
|
| 55 |
+
{%- for req_item in item_value -%}
|
| 56 |
+
<|"|>{{- req_item -}}<|"|>
|
| 57 |
+
{%- if not loop.last %},{% endif -%}
|
| 58 |
+
{%- endfor -%}
|
| 59 |
+
]
|
| 60 |
+
{%- elif item_key == 'type' -%}
|
| 61 |
+
{%- if item_value is string -%}
|
| 62 |
+
type:{{ format_argument(item_value | upper) }}
|
| 63 |
+
{%- else -%}
|
| 64 |
+
type:{{ format_argument(item_value | map('upper') | list) }}
|
| 65 |
+
{%- endif -%}
|
| 66 |
+
{%- else -%}
|
| 67 |
+
{{ item_key }}:{{ format_argument(item_value) }}
|
| 68 |
+
{%- endif -%}
|
| 69 |
+
{%- endif -%}
|
| 70 |
+
{%- endfor -%}
|
| 71 |
+
}
|
| 72 |
+
{%- endif -%}
|
| 73 |
+
{%- endif -%}
|
| 74 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 75 |
+
type:<|"|>{{ value['type'] | upper }}<|"|>}
|
| 76 |
+
{%- endif -%}
|
| 77 |
+
{%- endfor -%}
|
| 78 |
+
{%- endmacro -%}
|
| 79 |
+
{%- macro format_function_declaration(tool_data) -%}
|
| 80 |
+
declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|>
|
| 81 |
+
{%- set params = tool_data['function']['parameters'] -%}
|
| 82 |
+
{%- if params -%}
|
| 83 |
+
,parameters:{
|
| 84 |
+
{%- if params['properties'] -%}
|
| 85 |
+
properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
|
| 86 |
+
{%- endif -%}
|
| 87 |
+
{%- if params['required'] -%}
|
| 88 |
+
required:[
|
| 89 |
+
{%- for item in params['required'] -%}
|
| 90 |
+
<|"|>{{- item -}}<|"|>
|
| 91 |
+
{{- ',' if not loop.last -}}
|
| 92 |
+
{%- endfor -%}
|
| 93 |
+
],
|
| 94 |
+
{%- endif -%}
|
| 95 |
+
{%- if params['type'] -%}
|
| 96 |
+
type:<|"|>{{- params['type'] | upper -}}<|"|>}
|
| 97 |
+
{%- endif -%}
|
| 98 |
+
{%- endif -%}
|
| 99 |
+
{%- if 'response' in tool_data['function'] -%}
|
| 100 |
+
{%- set response_declaration = tool_data['function']['response'] -%}
|
| 101 |
+
,response:{
|
| 102 |
+
{%- if response_declaration['description'] -%}
|
| 103 |
+
description:<|"|>{{- response_declaration['description'] -}}<|"|>,
|
| 104 |
+
{%- endif -%}
|
| 105 |
+
{%- if response_declaration['type'] | upper == 'OBJECT' -%}
|
| 106 |
+
type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>}
|
| 107 |
+
{%- endif -%}
|
| 108 |
+
{%- endif -%}
|
| 109 |
+
}
|
| 110 |
+
{%- endmacro -%}
|
| 111 |
+
{%- macro format_argument(argument, escape_keys=True) -%}
|
| 112 |
+
{%- if argument is string -%}
|
| 113 |
+
{{- '<|"|>' + argument + '<|"|>' -}}
|
| 114 |
+
{%- elif argument is boolean -%}
|
| 115 |
+
{{- 'true' if argument else 'false' -}}
|
| 116 |
+
{%- elif argument is mapping -%}
|
| 117 |
+
{{- '{' -}}
|
| 118 |
+
{%- set ns = namespace(found_first=false) -%}
|
| 119 |
+
{%- for key, value in argument | dictsort -%}
|
| 120 |
+
{%- if ns.found_first %},{% endif -%}
|
| 121 |
+
{%- set ns.found_first = true -%}
|
| 122 |
+
{%- if escape_keys -%}
|
| 123 |
+
{{- '<|"|>' + key + '<|"|>' -}}
|
| 124 |
+
{%- else -%}
|
| 125 |
+
{{- key -}}
|
| 126 |
+
{%- endif -%}
|
| 127 |
+
:{{- format_argument(value, escape_keys=escape_keys) -}}
|
| 128 |
+
{%- endfor -%}
|
| 129 |
+
{{- '}' -}}
|
| 130 |
+
{%- elif argument is sequence -%}
|
| 131 |
+
{{- '[' -}}
|
| 132 |
+
{%- for item in argument -%}
|
| 133 |
+
{{- format_argument(item, escape_keys=escape_keys) -}}
|
| 134 |
+
{%- if not loop.last %},{% endif -%}
|
| 135 |
+
{%- endfor -%}
|
| 136 |
+
{{- ']' -}}
|
| 137 |
+
{%- else -%}
|
| 138 |
+
{{- argument -}}
|
| 139 |
+
{%- endif -%}
|
| 140 |
+
{%- endmacro -%}
|
| 141 |
+
{%- macro strip_thinking(text) -%}
|
| 142 |
+
{%- set ns = namespace(result='') -%}
|
| 143 |
+
{%- for part in text.split('<channel|>') -%}
|
| 144 |
+
{%- if '<|channel>' in part -%}
|
| 145 |
+
{%- set ns.result = ns.result + part.split('<|channel>')[0] -%}
|
| 146 |
+
{%- else -%}
|
| 147 |
+
{%- set ns.result = ns.result + part -%}
|
| 148 |
+
{%- endif -%}
|
| 149 |
+
{%- endfor -%}
|
| 150 |
+
{{- ns.result | trim -}}
|
| 151 |
+
{%- endmacro -%}
|
| 152 |
+
|
| 153 |
+
{%- set ns = namespace(prev_message_type=None) -%}
|
| 154 |
+
{%- set loop_messages = messages -%}
|
| 155 |
+
{{ bos_token }}
|
| 156 |
+
{#- Handle System/Tool Definitions Block -#}
|
| 157 |
+
{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}
|
| 158 |
+
{{- '<|turn>system\n' -}}
|
| 159 |
+
|
| 160 |
+
{#- Inject Thinking token at the very top of the FIRST system turn -#}
|
| 161 |
+
{%- if enable_thinking is defined and enable_thinking -%}
|
| 162 |
+
{{- '<|think|>' -}}
|
| 163 |
+
{%- set ns.prev_message_type = 'think' -%}
|
| 164 |
+
{%- endif -%}
|
| 165 |
+
|
| 166 |
+
{%- if messages[0]['role'] in ['system', 'developer'] -%}
|
| 167 |
+
{{- messages[0]['content'] | trim -}}
|
| 168 |
+
{%- set loop_messages = messages[1:] -%}
|
| 169 |
+
{%- endif -%}
|
| 170 |
+
|
| 171 |
+
{%- if tools -%}
|
| 172 |
+
{%- for tool in tools %}
|
| 173 |
+
{{- '<|tool>' -}}
|
| 174 |
+
{{- format_function_declaration(tool) | trim -}}
|
| 175 |
+
{{- '<tool|>' -}}
|
| 176 |
+
{%- endfor %}
|
| 177 |
+
{%- set ns.prev_message_type = 'tool' -%}
|
| 178 |
+
{%- endif -%}
|
| 179 |
+
|
| 180 |
+
{{- '<turn|>\n' -}}
|
| 181 |
+
{%- endif %}
|
| 182 |
+
|
| 183 |
+
{#- Loop through messages -#}
|
| 184 |
+
{%- for message in loop_messages -%}
|
| 185 |
+
{%- set ns.prev_message_type = None -%}
|
| 186 |
+
{%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
|
| 187 |
+
{{- '<|turn>' + role + '\n' }}
|
| 188 |
+
|
| 189 |
+
{%- if message['tool_calls'] -%}
|
| 190 |
+
{%- for tool_call in message['tool_calls'] -%}
|
| 191 |
+
{%- set function = tool_call['function'] -%}
|
| 192 |
+
{{- '<|tool_call>call:' + function['name'] + '{' -}}
|
| 193 |
+
{%- if function['arguments'] is mapping -%}
|
| 194 |
+
{%- set ns_args = namespace(found_first=false) -%}
|
| 195 |
+
{%- for key, value in function['arguments'] | dictsort -%}
|
| 196 |
+
{%- if ns_args.found_first %},{% endif -%}
|
| 197 |
+
{%- set ns_args.found_first = true -%}
|
| 198 |
+
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
| 199 |
+
{%- endfor -%}
|
| 200 |
+
{%- elif function['arguments'] is string -%}
|
| 201 |
+
{{- function['arguments'] -}}
|
| 202 |
+
{%- endif -%}
|
| 203 |
+
{{- '}<tool_call|>' -}}
|
| 204 |
+
{%- endfor -%}
|
| 205 |
+
{%- set ns.prev_message_type = 'tool_call' -%}
|
| 206 |
+
{%- endif -%}
|
| 207 |
+
|
| 208 |
+
{%- if message['tool_responses'] -%}
|
| 209 |
+
{#- Tool Response handling -#}
|
| 210 |
+
{%- for tool_response in message['tool_responses'] -%}
|
| 211 |
+
{{- '<|tool_response>' -}}
|
| 212 |
+
{%- if tool_response['response'] is mapping -%}
|
| 213 |
+
{{- 'response:' + tool_response['name'] | default('unknown') + '{' -}}
|
| 214 |
+
{%- for key, value in tool_response['response'] | dictsort -%}
|
| 215 |
+
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
| 216 |
+
{%- if not loop.last %},{% endif -%}
|
| 217 |
+
{%- endfor -%}
|
| 218 |
+
{{- '}' -}}
|
| 219 |
+
{%- else -%}
|
| 220 |
+
{{- 'response:' + tool_response['name'] | default('unknown') + '{value:' + format_argument(tool_response['response'], escape_keys=False) + '}' -}}
|
| 221 |
+
{%- endif -%}
|
| 222 |
+
{{- '<tool_response|>' -}}
|
| 223 |
+
{%- endfor -%}
|
| 224 |
+
{%- set ns.prev_message_type = 'tool_response' -%}
|
| 225 |
+
{%- endif -%}
|
| 226 |
+
|
| 227 |
+
{%- if message['content'] is string -%}
|
| 228 |
+
{%- if role == 'model' -%}
|
| 229 |
+
{{- strip_thinking(message['content']) -}}
|
| 230 |
+
{%- else -%}
|
| 231 |
+
{{- message['content'] | trim -}}
|
| 232 |
+
{%- endif -%}
|
| 233 |
+
{%- elif message['content'] is sequence -%}
|
| 234 |
+
{%- for item in message['content'] -%}
|
| 235 |
+
{%- if item['type'] == 'text' -%}
|
| 236 |
+
{%- if role == 'model' -%}
|
| 237 |
+
{{- strip_thinking(item['text']) -}}
|
| 238 |
+
{%- else -%}
|
| 239 |
+
{{- item['text'] | trim -}}
|
| 240 |
+
{%- endif -%}
|
| 241 |
+
{%- elif item['type'] == 'image' -%}
|
| 242 |
+
{{- '\n\n<|image|>\n\n' -}}
|
| 243 |
+
{%- set ns.prev_message_type = 'image' -%}
|
| 244 |
+
{%- elif item['type'] == 'audio' -%}
|
| 245 |
+
{{- '<|audio|>' -}}
|
| 246 |
+
{%- set ns.prev_message_type = 'audio' -%}
|
| 247 |
+
{%- elif item['type'] == 'video' -%}
|
| 248 |
+
{{- '\n\n<|video|>\n\n' -}}
|
| 249 |
+
{%- set ns.prev_message_type = 'video' -%}
|
| 250 |
+
{%- endif -%}
|
| 251 |
+
{%- endfor -%}
|
| 252 |
+
{%- endif -%}
|
| 253 |
+
|
| 254 |
+
{%- if not (message['tool_responses'] and not message['content']) -%}
|
| 255 |
+
{{- '<turn|>\n' -}}
|
| 256 |
+
{%- endif -%}
|
| 257 |
+
{%- endfor -%}
|
| 258 |
+
|
| 259 |
+
{%- if add_generation_prompt -%}
|
| 260 |
+
{%- if ns.prev_message_type != 'tool_response' -%}
|
| 261 |
+
{{- '<|turn>model\n' -}}
|
| 262 |
+
{%- endif -%}
|
| 263 |
+
{%- if not enable_thinking | default(false) -%}
|
| 264 |
+
{{- '<|channel>thought\n<channel|>' -}}
|
| 265 |
+
{%- endif -%}
|
| 266 |
+
{%- endif -%}
|
config.json
ADDED
|
@@ -0,0 +1,177 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Gemma4ForConditionalGeneration"
|
| 4 |
+
],
|
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| 34 |
+
"etd_token": "<tool|>",
|
| 35 |
+
"etr_token": "<tool_response|>",
|
| 36 |
+
"image_token": "<|image|>",
|
| 37 |
+
"soc_token": "<|channel>",
|
| 38 |
+
"sot_token": "<|turn>",
|
| 39 |
+
"stc_token": "<|tool_call>",
|
| 40 |
+
"std_token": "<|tool>",
|
| 41 |
+
"str_token": "<|tool_response>",
|
| 42 |
+
"think_token": "<|think|>"
|
| 43 |
+
},
|
| 44 |
+
"pad_token": "<pad>",
|
| 45 |
+
"padding_side": "left",
|
| 46 |
+
"processor_class": "Gemma4Processor",
|
| 47 |
+
"response_schema": {
|
| 48 |
+
"properties": {
|
| 49 |
+
"content": {
|
| 50 |
+
"type": "string"
|
| 51 |
+
},
|
| 52 |
+
"role": {
|
| 53 |
+
"const": "assistant"
|
| 54 |
+
},
|
| 55 |
+
"thinking": {
|
| 56 |
+
"type": "string"
|
| 57 |
+
},
|
| 58 |
+
"tool_calls": {
|
| 59 |
+
"items": {
|
| 60 |
+
"properties": {
|
| 61 |
+
"function": {
|
| 62 |
+
"properties": {
|
| 63 |
+
"arguments": {
|
| 64 |
+
"additionalProperties": {},
|
| 65 |
+
"type": "object",
|
| 66 |
+
"x-parser": "gemma4-tool-call"
|
| 67 |
+
},
|
| 68 |
+
"name": {
|
| 69 |
+
"type": "string"
|
| 70 |
+
}
|
| 71 |
+
},
|
| 72 |
+
"type": "object",
|
| 73 |
+
"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
|
| 74 |
+
},
|
| 75 |
+
"type": {
|
| 76 |
+
"const": "function"
|
| 77 |
+
}
|
| 78 |
+
},
|
| 79 |
+
"type": "object"
|
| 80 |
+
},
|
| 81 |
+
"type": "array",
|
| 82 |
+
"x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>"
|
| 83 |
+
}
|
| 84 |
+
},
|
| 85 |
+
"type": "object",
|
| 86 |
+
"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<content>(?:(?!\\<\\|tool_call\\>)(?!\\<turn\\|\\>).)+)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?:\\<turn\\|\\>)?"
|
| 87 |
+
},
|
| 88 |
+
"soc_token": "<|channel>",
|
| 89 |
+
"sot_token": "<|turn>",
|
| 90 |
+
"stc_token": "<|tool_call>",
|
| 91 |
+
"std_token": "<|tool>",
|
| 92 |
+
"str_token": "<|tool_response>",
|
| 93 |
+
"stride": 0,
|
| 94 |
+
"think_token": "<|think|>",
|
| 95 |
+
"tokenizer_class": "GemmaTokenizer",
|
| 96 |
+
"truncation_side": "right",
|
| 97 |
+
"truncation_strategy": "longest_first",
|
| 98 |
+
"unk_token": "<unk>"
|
| 99 |
+
}
|