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Model: thliang01/gemma-3-4B-T1-it-mlx-fp16 Source: Original Platform
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.gitattributes
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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94
README.md
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README.md
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---
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license: gemma
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language:
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- en
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- zh
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base_model: twinkle-ai/gemma-3-4B-T1-it
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library_name: transformers
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tags:
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- Taiwan
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- R.O.C
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- zhtw
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- SLM
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- Gemma-3
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- gemma3
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- mlx
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- mlx-my-repo
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datasets:
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- lianghsun/tw-reasoning-instruct
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- lianghsun/tw-contract-review-chat
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- minyichen/tw-instruct-R1-200k
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- minyichen/tw_mm_R1
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- minyichen/LongPaper_multitask_zh_tw_R1
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- nvidia/Nemotron-Instruction-Following-Chat-v1
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metrics:
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- accuracy
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model-index:
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- name: gemma-3-4B-T1-it
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results:
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: tmmlu+
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type: ikala/tmmluplus
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config: all
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split: test
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revision: c0e8ae955997300d5dbf0e382bf0ba5115f85e8c
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metrics:
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- type: accuracy
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value: 47.44
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name: single choice
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: mmlu
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type: cais/mmlu
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config: all
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split: test
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revision: c30699e
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metrics:
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- type: accuracy
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value: 59.13
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name: single choice
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- task:
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type: question-answering
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name: Single Choice Question
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dataset:
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name: tw-legal-benchmark-v1
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type: lianghsun/tw-legal-benchmark-v1
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config: all
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split: test
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revision: 66c3a5f
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metrics:
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- type: accuracy
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value: 44.18
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name: single choice
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---
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# thliang01/gemma-3-4B-T1-it-mlx-fp16
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The Model [thliang01/gemma-3-4B-T1-it-mlx-fp16](https://huggingface.co/thliang01/gemma-3-4B-T1-it-mlx-fp16) was converted to MLX format from [twinkle-ai/gemma-3-4B-T1-it](https://huggingface.co/twinkle-ai/gemma-3-4B-T1-it) using mlx-lm version **0.29.1**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("thliang01/gemma-3-4B-T1-it-mlx-fp16")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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3
added_tokens.json
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added_tokens.json
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{
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"<image_soft_token>": 262144
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}
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121
chat_template.jinja
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121
chat_template.jinja
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{{ bos_token }}
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{%- if tools %}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content'] %}
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{%- if messages[1]['role'] == 'user' %}
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{%- set first_user_message = messages[1]['content'] %}
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{%- set loop_messages = messages[2:] %}
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{%- else %}
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{%- set first_user_message = '' %}
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{%- set loop_messages = messages[1:] %}
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{%- endif %}
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{%- else %}
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{%- set system_message = "You are a function calling AI model." %}
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{%- if messages[0]['role'] == 'user' %}
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{%- set first_user_message = messages[0]['content'] %}
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{%- set loop_messages = messages[1:] %}
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{%- else %}
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{%- set first_user_message = '' %}
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{%- set loop_messages = messages %}
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{%- endif %}
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{%- endif %}
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<start_of_turn>user
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{{ system_message }} You are provided with function signatures within <tools> </tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
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<tools>
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{{ tools | tojson }}
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</tools>
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For each function call return a json object with function name and arguments within <tool_call> </tool_call> tags with the following schema:
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<tool_call>
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{"arguments": <args-dict>, "name": <function-name>}
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</tool_call>
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{%- if first_user_message %}
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{{ first_user_message }}
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{%- endif %}
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<end_of_turn>
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{% set ns = namespace(pairs=[]) %}
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{%- for message in loop_messages if message['role'] == 'assistant' and message.get('tool_calls') is defined %}
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{%- for tc in message['tool_calls'] %}
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{%- set ns.pairs = ns.pairs + [(tc.id, tc.function.name)] %}
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{%- endfor %}
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{%- endfor %}
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{%- set tool_call_map = dict(ns.pairs) %}
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{%- for message in loop_messages %}
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{%- set is_first_tool = message['role'] == 'tool' and (loop.index0 == 0 or loop_messages[loop.index0-1]['role'] != 'tool') %}
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{%- set is_last_tool = message['role'] == 'tool' and (loop.last or loop_messages[loop.index0+1]['role'] != 'tool') %}
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{% if message['role'] == 'user' %}
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<start_of_turn>user
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{{ message['content'] | trim }}<end_of_turn>
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{% elif message['role'] == 'assistant' -%}
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<start_of_turn>model
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{% if message.get('tool_calls') is defined and message.get('tool_calls') %}
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{%- for tool_call in message['tool_calls'] %}
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<tool_call>
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{"name": "{{ tool_call['function']['name'] }}", "arguments": {{ tool_call['function']['arguments'] | tojson }}}
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</tool_call>{% if not loop.last %}
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{% endif %}
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{%- endfor %}
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{%- else %}
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{{ message['content'] | trim }}
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{%- endif %}
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<end_of_turn>
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{% elif message['role'] == 'tool' %}
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{%- if is_first_tool %}
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<start_of_turn>tool
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{% endif %}
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<tool_response>
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{{ {"name": tool_call_map.get(message.get('tool_call_id', ''), "unknown"), "content": message['content']} | tojson }}
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</tool_response>
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{%- if not is_last_tool %}
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{% else %}
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<end_of_turn>
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{% endif %}
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{%- endif -%}
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{%- endfor %}{% if add_generation_prompt %}
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<start_of_turn>model
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{% endif %}
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{%- else %}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'image' -%}
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{{ '<img>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>\n' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model\n'}}
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{%- endif -%}
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{%- endif %}
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73
config.json
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config.json
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{
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"_sliding_window_pattern": 6,
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"architectures": [
|
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"Gemma3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"attn_logit_softcapping": null,
|
||||
"bos_token_id": 2,
|
||||
"cache_implementation": "hybrid",
|
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"eos_token_id": 1,
|
||||
"final_logit_softcapping": null,
|
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"head_dim": 256,
|
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"hidden_activation": "gelu_pytorch_tanh",
|
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"hidden_size": 2560,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 10240,
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"layer_types": [
|
||||
"sliding_attention",
|
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"sliding_attention",
|
||||
"sliding_attention",
|
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"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
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"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention"
|
||||
],
|
||||
"max_position_embeddings": 131072,
|
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"model_type": "gemma3_text",
|
||||
"num_attention_heads": 8,
|
||||
"num_hidden_layers": 34,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 0,
|
||||
"query_pre_attn_scalar": 256,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_local_base_freq": 10000.0,
|
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"rope_scaling": {
|
||||
"factor": 8.0,
|
||||
"rope_type": "linear"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 1024,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.55.2",
|
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"use_cache": false,
|
||||
"vocab_size": 262208
|
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}
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12
generation_config.json
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generation_config.json
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{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 2,
|
||||
"cache_implementation": "hybrid",
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
1,
|
||||
106
|
||||
],
|
||||
"pad_token_id": 0,
|
||||
"transformers_version": "4.55.2"
|
||||
}
|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"boi_token": "<start_of_image>",
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eoi_token": "<end_of_image>",
|
||||
"eos_token": {
|
||||
"content": "<end_of_turn>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"image_token": "<image_soft_token>",
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
|
||||
size 33384568
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
|
||||
size 4689074
|
||||
51347
tokenizer_config.json
Normal file
51347
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user