Add files using upload-large-folder tool
Browse files- README.md +52 -0
- config.json +9 -8
- generation_config.json +1 -1
- tokenizer_config.json +27 -49
README.md
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@@ -117,6 +117,58 @@ Gemma 4 models handle a broad range of tasks across text, vision, and audio. Key
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* **Multilingual** – Out-of-the-box support for 35+ languages, pre-trained on 140+ languages.
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* **Audio** (E2B and E4B only) – Automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages.
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## **Best Practices**
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For the best performance, use these configurations and best practices:
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* **Multilingual** – Out-of-the-box support for 35+ languages, pre-trained on 140+ languages.
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* **Audio** (E2B and E4B only) – Automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages.
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## Getting Started
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You can use all Gemma 4 models with the latest version of Transformers. To get started, install the necessary dependencies in your environment:
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`pip install -U transformers torch accelerate`
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Once you have everything installed, you can proceed to load the model with the code below:
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```python
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import torch
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from transformers import AutoProcessor, AutoModelForCausalLM
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MODEL_ID = "google/gemma-4-E4B-it"
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# Load model
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processor = AutoProcessor.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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dtype=torch.bfloat16,
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device_map="auto"
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)
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```
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Once the model is loaded, you can start generating output:
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```python
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# Prompt
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Write a short joke about saving RAM."},
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]
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# Process input
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text = processor.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False
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)
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inputs = processor(text=text, return_tensors="pt").to(model.device)
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input_len = inputs["input_ids"].shape[-1]
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# Generate output
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outputs = model.generate(**inputs, max_new_tokens=1024)
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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse thinking
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processor.parse_response(response)
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```
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To enable reasoning, set `enable_thinking=True` and the `parse_response` function will take care of parsing the thinking output.
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## **Best Practices**
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For the best performance, use these configurations and best practices:
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config.json
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"attention_logit_cap": 50.0,
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"chunk_size_feed_forward": 0,
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"conv_kernel_size": 5,
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"
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"gradient_clipping": 10000000000.0,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"audio_token_id": 258881,
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"boa_token_id": 256000,
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"boi_token_id": 255999,
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"
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"eoa_token_id": 258883,
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"eoa_token_index": 258883,
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"eoi_token_id": 258882,
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"eos_token_id":
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"image_token_id": 258880,
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"initializer_range": 0.02,
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"model_type": "gemma4",
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"pad_token_id": 0,
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"text_config": {
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attention_k_eq_v": false,
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"bos_token_id": 2,
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"
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"enable_moe_block": false,
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"eos_token_id": 1,
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"expert_intermediate_size": null,
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0.dev0",
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"unsloth_fixed": true,
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"video_token_id": 258884,
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"vision_config": {
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"_name_or_path": "",
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"attention_dropout": 0.0,
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"chunk_size_feed_forward": 0,
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"default_output_length": 280,
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"
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"global_head_dim": 64,
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"head_dim": 64,
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"hidden_activation": "gelu_pytorch_tanh",
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"use_clipped_linears": true
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},
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"vision_soft_tokens_per_image": 280
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}
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"attention_logit_cap": 50.0,
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"chunk_size_feed_forward": 0,
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"conv_kernel_size": 5,
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"dtype": "bfloat16",
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"gradient_clipping": 10000000000.0,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"audio_token_id": 258881,
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"boa_token_id": 256000,
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"boi_token_id": 255999,
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"dtype": "bfloat16",
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"eoa_token_id": 258883,
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"eoa_token_index": 258883,
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"eoi_token_id": 258882,
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"eos_token_id": [
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],
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"image_token_id": 258880,
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"initializer_range": 0.02,
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"model_type": "gemma4",
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"text_config": {
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attention_k_eq_v": false,
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"bos_token_id": 2,
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"dtype": "bfloat16",
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"enable_moe_block": false,
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"eos_token_id": 1,
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"expert_intermediate_size": null,
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0.dev0",
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"video_token_id": 258884,
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"vision_config": {
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"_name_or_path": "",
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"attention_dropout": 0.0,
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"chunk_size_feed_forward": 0,
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"default_output_length": 280,
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"dtype": "bfloat16",
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"global_head_dim": 64,
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"head_dim": 64,
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"hidden_activation": "gelu_pytorch_tanh",
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"use_clipped_linears": true
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},
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"vision_soft_tokens_per_image": 280
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}
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generation_config.json
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"top_k": 64,
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"top_p": 0.95,
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"transformers_version": "5.5.0.dev0"
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}
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"top_k": 64,
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"top_p": 0.95,
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"transformers_version": "5.5.0.dev0"
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}
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tokenizer_config.json
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"eoa_token": "<audio|>",
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"eoc_token": "<channel|>",
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"eoi_token": "<image|>",
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"eos_token": "<
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"eot_token": "<turn|>",
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"escape_token": "<|\"|>",
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"etc_token": "<tool_call|>",
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"<|video|>"
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],
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"image_token": "<|image|>",
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"is_local": true,
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"mask_token": "<mask>",
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"model_max_length":
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"model_specific_special_tokens": {
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"audio_token": "<|audio|>",
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"boa_token": "<|audio>",
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"boi_token": "<|image>",
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"eoa_token": "<audio|>",
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"eoc_token": "<channel|>",
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"eoi_token": "<image|>",
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"eot_token": "<turn|>",
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"escape_token": "<|\"|>",
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"etc_token": "<tool_call|>",
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"etd_token": "<tool|>",
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"etr_token": "<tool_response|>",
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"image_token": "<|image|>",
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"soc_token": "<|channel>",
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"sot_token": "<|turn>",
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"stc_token": "<|tool_call>",
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"std_token": "<|tool>",
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"str_token": "<|tool_response>",
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"think_token": "<|think|>"
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},
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"pad_token": "<pad>",
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"padding_side": "left",
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"processor_class": "Gemma4Processor",
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"response_schema": {
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"properties": {
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"role": {
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"const": "assistant"
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},
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"thinking": {
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"type": "string"
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},
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"tool_calls": {
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"items": {
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"properties": {
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"function": {
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"properties": {
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"arguments": {
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"type": "string",
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"x-mapping-regex": {
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"(\\{|,)\\s*([a-zA-Z_]\\w+):": "\\1\"\\2\":",
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"<\\|\\\"\\|>": "\""
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},
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"x-regex": "call:[^{]+(\\{.*\\})"
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},
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"name": {
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"type": "string"
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}
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}
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"type": "object"
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},
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"type": {
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"const": "function"
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}
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}
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},
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"type": "array",
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"x-regex-iterator": "<\\|tool_call\\>(.*?)<tool_call\\|>"
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}
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},
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"
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},
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"soc_token": "<|channel>",
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"sot_token": "<|turn>",
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"str_token": "<|tool_response>",
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"think_token": "<|think|>",
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"tokenizer_class": "GemmaTokenizer",
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"unk_token": "<unk>"
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"chat_template": "{%- macro format_parameters(properties, required) -%}\n {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}\n {%- set ns = namespace(found_first=false) -%}\n {%- for key, value in properties | dictsort -%}\n {%- set add_comma = false -%}\n {%- if key not in standard_keys -%}\n {%- if ns.found_first %},{% endif -%}\n {%- set ns.found_first = true -%}\n {{ key }}:{\n {%- if value['description'] -%}\n description:<|\"|>{{ value['description'] }}<|\"|>\n {%- set add_comma = true -%}\n {%- endif -%}\n {%- if value['nullable'] %}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n nullable:true\n {%- endif -%}\n {%- if value['type'] | upper == 'STRING' -%}\n {%- if value['enum'] -%}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n enum:{{ format_argument(value['enum']) }}\n {%- endif -%}\n {%- elif value['type'] | upper == 'OBJECT' -%}\n ,properties:{\n {%- if value['properties'] is defined and value['properties'] is mapping -%}\n {{- format_parameters(value['properties'], value['required'] | default([])) -}}\n {%- elif value is mapping -%}\n {{- format_parameters(value, value['required'] | default([])) -}}\n {%- endif -%}\n }\n {%- if value['required'] -%}\n ,required:[\n {%- for item in value['required'] | default([]) -%}\n <|\"|>{{- item -}}<|\"|>\n {%- if not loop.last %},{% endif -%}\n {%- endfor -%}\n ]\n {%- endif -%}\n {%- elif value['type'] | upper == 'ARRAY' -%}\n {%- if value['items'] is mapping and value['items'] -%}\n ,items:{\n {%- set ns_items = namespace(found_first=false) -%}\n {%- for item_key, item_value in value['items'] | dictsort -%}\n {%- if item_value is not none -%}\n {%- if ns_items.found_first %},{% endif -%}\n {%- set ns_items.found_first = true -%}\n {%- if item_key == 'properties' -%}\n properties:{\n {%- if item_value is mapping -%}\n {{- format_parameters(item_value, value['items']['required'] | default([])) -}}\n {%- endif -%}\n }\n {%- elif item_key == 'required' -%}\n required:[\n {%- for req_item in item_value -%}\n <|\"|>{{- req_item -}}<|\"|>\n {%- if not loop.last %},{% endif -%}\n {%- endfor -%}\n ]\n {%- elif item_key == 'type' -%}\n {%- if item_value is string -%}\n type:{{ format_argument(item_value | upper) }}\n {%- else -%}\n type:{{ format_argument(item_value | map('upper') | list) }}\n {%- endif -%}\n {%- else -%}\n {{ item_key }}:{{ format_argument(item_value) }}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n }\n {%- endif -%}\n {%- endif -%}\n {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n type:<|\"|>{{ value['type'] | upper }}<|\"|>}\n {%- endif -%}\n {%- endfor -%}\n{%- endmacro -%}\n{%- macro format_function_declaration(tool_data) -%}\n declaration:{{- tool_data['function']['name'] -}}{description:<|\"|>{{- tool_data['function']['description'] -}}<|\"|>\n {%- set params = tool_data['function']['parameters'] -%}\n {%- if params -%}\n ,parameters:{\n {%- if params['properties'] -%}\n properties:{ {{- format_parameters(params['properties'], params['required']) -}} },\n {%- endif -%}\n {%- if params['required'] -%}\n required:[\n {%- for item in params['required'] -%}\n <|\"|>{{- item -}}<|\"|>\n {{- ',' if not loop.last -}}\n {%- endfor -%}\n ],\n {%- endif -%}\n {%- if params['type'] -%}\n type:<|\"|>{{- params['type'] | upper -}}<|\"|>}\n {%- endif -%}\n {%- endif -%}\n {%- if 'response' in tool_data['function'] -%}\n {%- set response_declaration = tool_data['function']['response'] -%}\n ,response:{\n {%- if response_declaration['description'] -%}\n description:<|\"|>{{- response_declaration['description'] -}}<|\"|>,\n {%- endif -%}\n {%- if response_declaration['type'] | upper == 'OBJECT' -%}\n type:<|\"|>{{- response_declaration['type'] | upper -}}<|\"|>}\n {%- endif -%}\n {%- endif -%}\n }\n{%- endmacro -%}\n{%- macro format_argument(argument, escape_keys=True) -%}\n {%- if argument is string -%}\n {{- '<|\"|>' + argument + '<|\"|>' -}}\n {%- elif argument is boolean -%}\n {{- 'true' if argument else 'false' -}}\n {%- elif argument is mapping -%}\n {{- '{' -}}\n {%- set ns = namespace(found_first=false) -%}\n {%- for key, value in argument | dictsort -%}\n {%- if ns.found_first %},{% endif -%}\n {%- set ns.found_first = true -%}\n {%- if escape_keys -%}\n {{- '<|\"|>' + key + '<|\"|>' -}}\n {%- else -%}\n {{- key -}}\n {%- endif -%}\n :{{- format_argument(value, escape_keys=escape_keys) -}}\n {%- endfor -%}\n {{- '}' -}}\n {%- elif argument is sequence -%}\n {{- '[' -}}\n {%- for item in argument -%}\n {{- format_argument(item, escape_keys=escape_keys) -}}\n {%- if not loop.last %},{% endif -%}\n {%- endfor -%}\n {{- ']' -}}\n {%- else -%}\n {{- argument -}}\n {%- endif -%}\n{%- endmacro -%}\n{%- macro strip_thinking(text) -%}\n {%- set ns = namespace(result='') -%}\n {%- for part in text.split('<channel|>') -%}\n {%- if '<|channel>' in part -%}\n {%- set ns.result = ns.result + part.split('<|channel>')[0] -%}\n {%- else -%}\n {%- set ns.result = ns.result + part -%}\n {%- endif -%}\n {%- endfor -%}\n {{- ns.result | trim -}}\n{%- endmacro -%}\n\n{%- set ns = namespace(prev_message_type=None) -%}\n{%- set loop_messages = messages -%}\n{{ bos_token }}\n{#- Handle System/Tool Definitions Block -#}\n{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}\n {{- '<|turn>system\\n' -}}\n\n {#- Inject Thinking token at the very top of the FIRST system turn -#}\n {%- if enable_thinking is defined and enable_thinking -%}\n {{- '<|think|>' -}}\n {%- set ns.prev_message_type = 'think' -%}\n {%- endif -%}\n\n {%- if messages[0]['role'] in ['system', 'developer'] -%}\n {{- messages[0]['content'] | trim -}}\n {%- set loop_messages = messages[1:] -%}\n {%- endif -%}\n\n {%- if tools -%}\n {%- for tool in tools %}\n {{- '<|tool>' -}}\n {{- format_function_declaration(tool) | trim -}}\n {{- '<tool|>' -}}\n {%- endfor %}\n {%- set ns.prev_message_type = 'tool' -%}\n {%- endif -%}\n\n {{- '<turn|>\\n' -}}\n{%- endif %}\n\n{#- Loop through messages -#}\n{%- for message in loop_messages -%}\n {%- set ns.prev_message_type = None -%}\n {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}\n {{- '<|turn>' + role + '\\n' }}\n\n {%- if message['tool_calls'] -%}\n {%- for tool_call in message['tool_calls'] -%}\n {%- set function = tool_call['function'] -%}\n {{- '<|tool_call>call:' + function['name'] + '{' -}}\n {%- if function['arguments'] is mapping -%}\n {%- set ns_args = namespace(found_first=false) -%}\n {%- for key, value in function['arguments'] | dictsort -%}\n {%- if ns_args.found_first %},{% endif -%}\n {%- set ns_args.found_first = true -%}\n {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n {%- endfor -%}\n {%- elif function['arguments'] is string -%}\n {{- function['arguments'] -}}\n {%- endif -%}\n {{- '}<tool_call|>' -}}\n {%- endfor -%}\n {%- set ns.prev_message_type = 'tool_call' -%}\n {%- endif -%}\n\n {%- if message['tool_responses'] -%}\n {#- Tool Response handling -#}\n {%- for tool_response in message['tool_responses'] -%}\n {{- '<|tool_response>' -}}\n {%- if tool_response['response'] is mapping -%}\n {{- 'response:' + tool_response['name'] | default('unknown') + '{' -}}\n {%- for key, value in tool_response['response'] | dictsort -%}\n {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n {%- if not loop.last %},{% endif -%}\n {%- endfor -%}\n {{- '}' -}}\n {%- else -%}\n {{- 'response:' + tool_response['name'] | default('unknown') + '{value:' + format_argument(tool_response['response'], escape_keys=False) + '}' -}}\n {%- endif -%}\n {{- '<tool_response|>' -}}\n {%- endfor -%}\n {%- set ns.prev_message_type = 'tool_response' -%}\n {%- endif -%}\n\n {%- if message['content'] is string -%}\n {%- if role == 'model' -%}\n {{- strip_thinking(message['content']) -}}\n {%- else -%}\n {{- message['content'] | trim -}}\n {%- endif -%}\n {%- elif message['content'] is sequence -%}\n {%- for item in message['content'] -%}\n {%- if item['type'] == 'text' -%}\n {%- if role == 'model' -%}\n {{- strip_thinking(item['text']) -}}\n {%- else -%}\n {{- item['text'] | trim -}}\n {%- endif -%}\n {%- elif item['type'] == 'image' -%}\n {{- '\\n\\n<|image|>\\n\\n' -}}\n {%- set ns.prev_message_type = 'image' -%}\n {%- elif item['type'] == 'audio' -%}\n {{- '<|audio|>' -}}\n {%- set ns.prev_message_type = 'audio' -%}\n {%- elif item['type'] == 'video' -%}\n {{- '\\n\\n<|video|>\\n\\n' -}}\n {%- set ns.prev_message_type = 'video' -%}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n\n {%- if not (message['tool_responses'] and not message['content']) -%}\n {{- '<turn|>\\n' -}}\n {%- endif -%}\n{%- endfor -%}\n\n{%- if add_generation_prompt -%}\n {%- if ns.prev_message_type != 'tool_response' -%}\n {{- '<|turn>model\\n' -}}\n {%- endif -%}\n{%- endif -%}"
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| 96 |
-
}
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| 7 |
"eoa_token": "<audio|>",
|
| 8 |
"eoc_token": "<channel|>",
|
| 9 |
"eoi_token": "<image|>",
|
| 10 |
+
"eos_token": "<eos>",
|
| 11 |
"eot_token": "<turn|>",
|
| 12 |
"escape_token": "<|\"|>",
|
| 13 |
"etc_token": "<tool_call|>",
|
|
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|
| 17 |
"<|video|>"
|
| 18 |
],
|
| 19 |
"image_token": "<|image|>",
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|
| 20 |
"mask_token": "<mask>",
|
| 21 |
+
"model_max_length": 1000000000000000019884624838656,
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|
| 22 |
"pad_token": "<pad>",
|
| 23 |
"padding_side": "left",
|
| 24 |
"processor_class": "Gemma4Processor",
|
| 25 |
"response_schema": {
|
| 26 |
+
"type": "object",
|
| 27 |
"properties": {
|
| 28 |
"role": {
|
| 29 |
"const": "assistant"
|
| 30 |
},
|
| 31 |
"thinking": {
|
| 32 |
+
"type": "string"
|
| 33 |
+
},
|
| 34 |
+
"content": {
|
| 35 |
+
"type": "string"
|
| 36 |
},
|
| 37 |
"tool_calls": {
|
| 38 |
+
"x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>",
|
| 39 |
+
"type": "array",
|
| 40 |
"items": {
|
| 41 |
+
"type": "object",
|
| 42 |
"properties": {
|
| 43 |
+
"type": {
|
| 44 |
+
"const": "function"
|
| 45 |
+
},
|
| 46 |
"function": {
|
| 47 |
+
"type": "object",
|
| 48 |
+
"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})",
|
| 49 |
"properties": {
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|
| 50 |
"name": {
|
| 51 |
+
"type": "string"
|
| 52 |
+
},
|
| 53 |
+
"arguments": {
|
| 54 |
+
"type": "object",
|
| 55 |
+
"x-parser": "gemma4-tool-call",
|
| 56 |
+
"additionalProperties": {}
|
| 57 |
}
|
| 58 |
+
}
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|
| 59 |
}
|
| 60 |
+
}
|
| 61 |
+
}
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|
| 62 |
}
|
| 63 |
},
|
| 64 |
+
"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<content>(?:(?!\\<\\|tool_call\\>)(?!\\<turn\\|\\>).)+)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?:\\<turn\\|\\>)?"
|
| 65 |
},
|
| 66 |
"soc_token": "<|channel>",
|
| 67 |
"sot_token": "<|turn>",
|
|
|
|
| 70 |
"str_token": "<|tool_response>",
|
| 71 |
"think_token": "<|think|>",
|
| 72 |
"tokenizer_class": "GemmaTokenizer",
|
| 73 |
+
"unk_token": "<unk>"
|
| 74 |
+
}
|
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|