Instructions to use google/gemma-4-E4B-it-qat-q4_0-unquantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-E4B-it-qat-q4_0-unquantized with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-E4B-it-qat-q4_0-unquantized") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-E4B-it-qat-q4_0-unquantized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add response_template to tokenizer_config.json
#3
by Rocketknight1 HF Staff - opened
- README.md +4 -4
- tokenizer_config.json +46 -0
README.md
CHANGED
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@@ -180,7 +180,7 @@ 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 output
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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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@@ -240,7 +240,7 @@ outputs = model.generate(**inputs, max_new_tokens=512)
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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse output
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processor.parse_response(response)
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```
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</details>
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@@ -298,7 +298,7 @@ outputs = model.generate(**inputs, max_new_tokens=512)
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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse output
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processor.parse_response(response)
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```
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</details>
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@@ -357,7 +357,7 @@ outputs = model.generate(**inputs, max_new_tokens=512)
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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse output
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processor.parse_response(response)
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```
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</details>
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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse output
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processor.parse_response(response, prefix=inputs["input_ids"])
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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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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse output
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processor.parse_response(response, prefix=inputs["input_ids"])
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```
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</details>
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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse output
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processor.parse_response(response, prefix=inputs["input_ids"])
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```
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</details>
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response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)
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# Parse output
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processor.parse_response(response, prefix=inputs["input_ids"])
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```
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</details>
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tokenizer_config.json
CHANGED
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@@ -85,6 +85,52 @@
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"type": "object",
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"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
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},
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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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"type": "object",
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"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
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},
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"response_template": {
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"defaults": {
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"role": "assistant"
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},
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"fields": {
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"content": {
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"close": [
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"<turn|>",
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"<|tool_response>",
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"<eos>"
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],
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"content": "text"
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},
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"thinking": {
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"close": "<channel|>",
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"content": "text",
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"open": "<|channel>thought\n"
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},
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"tool_calls": {
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"close": "<tool_call|>",
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"content": "json",
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"content_args": {
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"string_delims": [
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[
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"<|\"|>",
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"<|\"|>"
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]
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],
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"unquoted_keys": true
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},
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"open_pattern": "<\\|tool_call>call:(?P<name>\\w+)",
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"repeats": true,
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"transform": {
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"function": {
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"arguments": "{content}",
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"name": "{name}"
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},
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"type": "function"
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}
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}
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},
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"start_anchor": [
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"<|turn>model\n",
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"<tool_response|>"
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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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"stc_token": "<|tool_call>",
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