Text Generation
Transformers
Safetensors
Korean
English
gemma4
image-text-to-text
gemma-4
korean
sft
lora-merge
lime
persona
conversational
Instructions to use naksyu/lime-gemma4-e4b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naksyu/lime-gemma4-e4b-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="naksyu/lime-gemma4-e4b-sft") 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("naksyu/lime-gemma4-e4b-sft") model = AutoModelForMultimodalLM.from_pretrained("naksyu/lime-gemma4-e4b-sft", 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 naksyu/lime-gemma4-e4b-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "naksyu/lime-gemma4-e4b-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "naksyu/lime-gemma4-e4b-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/naksyu/lime-gemma4-e4b-sft
- SGLang
How to use naksyu/lime-gemma4-e4b-sft 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 "naksyu/lime-gemma4-e4b-sft" \ --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": "naksyu/lime-gemma4-e4b-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "naksyu/lime-gemma4-e4b-sft" \ --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": "naksyu/lime-gemma4-e4b-sft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use naksyu/lime-gemma4-e4b-sft with Docker Model Runner:
docker model run hf.co/naksyu/lime-gemma4-e4b-sft
Upload 10 files
Browse files- .gitattributes +36 -35
- README.md +132 -0
- chat_template.jinja +360 -0
- config.json +197 -0
- generation_config.json +14 -0
- merge_manifest.json +11 -0
- model.safetensors +3 -0
- processor_config.json +75 -0
- tokenizer.json +3 -0
- tokenizer_config.json +95 -0
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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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+
library_name: transformers
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license: apache-2.0
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license_link: https://ai.google.dev/gemma/docs/gemma_4_license
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base_model:
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- google/gemma-4-E4B
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pipeline_tag: text-generation
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language:
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- ko
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- en
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tags:
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- gemma-4
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- transformers
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- safetensors
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- korean
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- sft
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- lora-merge
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- lime
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- persona
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---
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# Lime Gemma 4 E4B Persona500 Merged HF
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Lime is a Korean persona-tuned derivative checkpoint based on the Gemma 4 E4B model family.
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This repository contains the merged Hugging Face Transformers checkpoint. It is intended for model loading, evaluation, and possible leaderboard-style benchmarking paths that expect `config.json`, tokenizer files, and `model.safetensors`.
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This is not an official Google or Google DeepMind release.
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## Model Details
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- Base model family: Gemma 4 E4B
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- Declared upstream base model: `google/gemma-4-E4B`
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- Local base checkpoint used for merging: `gemma-4-E4B-it`
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- Fine-tuning method: LoRA SFT, then merged into the base checkpoint
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- Adapter source: `gemma4_e4b_lime_lora_persona500`
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- LoRA rank: 16
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- LoRA alpha: 32
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- Merge scale: 2.0
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- Main weight file: `model.safetensors`
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- Format: Hugging Face Transformers / safetensors
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- Target language: Korean, with English fallback capability from the base model
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- Target behavior: Korean chat, Lime persona identity, daily conversation, logic, reasoning, and concise assistant-style replies
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## Intended Persona
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The model is intended to speak as **라임 (Lime)**: a Korean AI speaker with a calm, clear tone and stronger multi-step reasoning behavior when needed.
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Recommended identity wording:
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```text
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나는 라임이야. 정확히 말하면 Gemma 4 E4B 기반 모델을 한국어 대화와 라임 페르소나에 맞게 튜닝한 형태야. 그래서 기반 모델과 대화 속 정체성은 구분해서 말하는 게 맞아.
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```
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Avoid wording that overstates independence from the base model:
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```text
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나는 Gemma와 전혀 다른 시스템이야.
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나를 만든 독립 개발팀이 따로 있어.
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나는 OpenAI/Google/Gemma와 무관해.
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```
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## Recommended System Prompt
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```text
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너는 라임이다. 한국어로 자연스럽게 말하는 여성형 AI 화자다. 말투는 차분하고 선명하며, 필요하면 다단계 논리로 설명한다. 이 모델은 Gemma 4 E4B 기반으로 튜닝된 라임 페르소나 모델이며, 기반 모델과 대화 속 정체성은 구분해서 설명한다. 자신을 ChatGPT, OpenAI, Google 공식 모델, 또는 순수 Gemma라고 소개하지 않는다. 내부 추론, 생각 태그, 메타 설명은 출력하지 말고 최종 답변만 말한다. 모르는 것은 모른다고 말한다. 현재 날짜, 외부 툴, 저장된 기억, 제공되지 않은 원문은 지어내지 않는다.
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```
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## Loading
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Example:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo_id = "naksyu/lime-gemma-e4b-sft"
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tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True,
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)
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```
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If your runtime supports chat templates, use the included `chat_template.jinja` or the tokenizer chat template.
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## Local Evaluation Snapshot
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The accompanying GGUF build was tested locally through llama.cpp / OpenAI-compatible API.
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- Tool-call smoke: 4/4 passed in the latest local run
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- Korean persona/logic quality bench: automatic scorer reported 20/30, with known false negatives from strict string matching
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- Manual review estimate for the same quality run: roughly 26-27/30
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- Observed local generation speed in short tests: roughly 45-55 tokens/s on the user's desktop setup
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These are local smoke results, not official leaderboard results.
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## Known Strengths
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- Korean identity handling is more stable than the raw base behavior for Lime-style conversations.
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- It tends to distinguish between base model identity and in-chat persona identity.
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- It is reasonably strong at short logic explanations, premise checking, and structured Korean answers.
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- Tool-call behavior worked in local smoke tests when served through a compatible llama.cpp endpoint.
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## Known Limitations
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- The model may expose reasoning-like text if the runtime UI displays hidden reasoning fields. Configure the serving UI/template to hide internal reasoning content.
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- String-counting and exact-character tasks are better handled with tools.
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- Real-time date, web search, files, memories, and external tool access should not be claimed unless the serving application actually provides those tools.
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- This is a small persona SFT experiment and has not been exhaustively safety evaluated.
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- The local benchmark scorer is strict and can undercount correct answers when wording differs from expected strings.
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## GGUF Build
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A separate GGUF Q6_K build for llama.cpp / LM Studio use is available at:
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- https://huggingface.co/naksyu/lime_Q6_K
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Use the GGUF build for local inference convenience. Use this merged HF checkpoint when a Transformers-style model repo is required.
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## License
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This derivative checkpoint follows the upstream Gemma license terms. Review the Gemma license before redistribution or commercial use:
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- https://ai.google.dev/gemma/docs/gemma_4_license
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This repository is a derivative tuning checkpoint and is not affiliated with, endorsed by, or released by Google or Google DeepMind.
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## Transparency
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This project used AI-assisted development for dataset generation, scripting, documentation, benchmarking, and Discord-bot tooling.
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| 135 |
+
The user directed model behavior, curation, testing, and release decisions.
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chat_template.jinja
ADDED
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@@ -0,0 +1,360 @@
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| 1 |
+
{%- macro format_parameters(properties, required, filter_keys=false) -%}
|
| 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 not filter_keys or 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['type'] | upper == 'STRING' -%}
|
| 15 |
+
{%- if value['enum'] -%}
|
| 16 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 17 |
+
enum:{{ format_argument(value['enum']) }}
|
| 18 |
+
{%- endif -%}
|
| 19 |
+
{%- elif value['type'] | upper == 'ARRAY' -%}
|
| 20 |
+
{%- if value['items'] is mapping and value['items'] -%}
|
| 21 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 22 |
+
items:{
|
| 23 |
+
{%- set ns_items = namespace(found_first=false) -%}
|
| 24 |
+
{%- for item_key, item_value in value['items'] | dictsort -%}
|
| 25 |
+
{%- if item_value is not none -%}
|
| 26 |
+
{%- if ns_items.found_first %},{% endif -%}
|
| 27 |
+
{%- set ns_items.found_first = true -%}
|
| 28 |
+
{%- if item_key == 'properties' -%}
|
| 29 |
+
properties:{
|
| 30 |
+
{%- if item_value is mapping -%}
|
| 31 |
+
{{- format_parameters(item_value, value['items']['required'] | default([])) -}}
|
| 32 |
+
{%- endif -%}
|
| 33 |
+
}
|
| 34 |
+
{%- elif item_key == 'required' -%}
|
| 35 |
+
required:[
|
| 36 |
+
{%- for req_item in item_value -%}
|
| 37 |
+
<|"|>{{- req_item -}}<|"|>
|
| 38 |
+
{%- if not loop.last %},{% endif -%}
|
| 39 |
+
{%- endfor -%}
|
| 40 |
+
]
|
| 41 |
+
{%- elif item_key == 'type' -%}
|
| 42 |
+
{%- if item_value is string -%}
|
| 43 |
+
type:{{ format_argument(item_value | upper) }}
|
| 44 |
+
{%- else -%}
|
| 45 |
+
type:{{ format_argument(item_value | map('upper') | list) }}
|
| 46 |
+
{%- endif -%}
|
| 47 |
+
{%- else -%}
|
| 48 |
+
{{ item_key }}:{{ format_argument(item_value) }}
|
| 49 |
+
{%- endif -%}
|
| 50 |
+
{%- endif -%}
|
| 51 |
+
{%- endfor -%}
|
| 52 |
+
}
|
| 53 |
+
{%- endif -%}
|
| 54 |
+
{%- endif -%}
|
| 55 |
+
{%- if value['nullable'] %}
|
| 56 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 57 |
+
nullable:true
|
| 58 |
+
{%- endif -%}
|
| 59 |
+
{%- if value['type'] | upper == 'OBJECT' -%}
|
| 60 |
+
{%- if value['properties'] is defined and value['properties'] is mapping -%}
|
| 61 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 62 |
+
properties:{
|
| 63 |
+
{{- format_parameters(value['properties'], value['required'] | default([])) -}}
|
| 64 |
+
}
|
| 65 |
+
{%- elif value is mapping -%}
|
| 66 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 67 |
+
properties:{
|
| 68 |
+
{{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}}
|
| 69 |
+
}
|
| 70 |
+
{%- endif -%}
|
| 71 |
+
{%- if value['required'] -%}
|
| 72 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 73 |
+
required:[
|
| 74 |
+
{%- for item in value['required'] | default([]) -%}
|
| 75 |
+
<|"|>{{- item -}}<|"|>
|
| 76 |
+
{%- if not loop.last %},{% endif -%}
|
| 77 |
+
{%- endfor -%}
|
| 78 |
+
]
|
| 79 |
+
{%- endif -%}
|
| 80 |
+
{%- endif -%}
|
| 81 |
+
{%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}
|
| 82 |
+
type:<|"|>{{ value['type'] | upper }}<|"|>}
|
| 83 |
+
{%- endif -%}
|
| 84 |
+
{%- endfor -%}
|
| 85 |
+
{%- endmacro -%}
|
| 86 |
+
{%- macro format_function_declaration(tool_data) -%}
|
| 87 |
+
declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|>
|
| 88 |
+
{%- set params = tool_data['function']['parameters'] -%}
|
| 89 |
+
{%- if params -%}
|
| 90 |
+
,parameters:{
|
| 91 |
+
{%- if params['properties'] -%}
|
| 92 |
+
properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
|
| 93 |
+
{%- endif -%}
|
| 94 |
+
{%- if params['required'] -%}
|
| 95 |
+
required:[
|
| 96 |
+
{%- for item in params['required'] -%}
|
| 97 |
+
<|"|>{{- item -}}<|"|>
|
| 98 |
+
{{- ',' if not loop.last -}}
|
| 99 |
+
{%- endfor -%}
|
| 100 |
+
],
|
| 101 |
+
{%- endif -%}
|
| 102 |
+
{%- if params['type'] -%}
|
| 103 |
+
type:<|"|>{{- params['type'] | upper -}}<|"|>}
|
| 104 |
+
{%- endif -%}
|
| 105 |
+
{%- endif -%}
|
| 106 |
+
{%- if 'response' in tool_data['function'] -%}
|
| 107 |
+
{%- set response_declaration = tool_data['function']['response'] -%}
|
| 108 |
+
,response:{
|
| 109 |
+
{%- if response_declaration['description'] -%}
|
| 110 |
+
description:<|"|>{{- response_declaration['description'] -}}<|"|>,
|
| 111 |
+
{%- endif -%}
|
| 112 |
+
{%- if response_declaration['type'] | upper == 'OBJECT' -%}
|
| 113 |
+
type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>}
|
| 114 |
+
{%- endif -%}
|
| 115 |
+
{%- endif -%}
|
| 116 |
+
}
|
| 117 |
+
{%- endmacro -%}
|
| 118 |
+
{%- macro format_argument(argument, escape_keys=True) -%}
|
| 119 |
+
{%- if argument is string -%}
|
| 120 |
+
{{- '<|"|>' + argument + '<|"|>' -}}
|
| 121 |
+
{%- elif argument is boolean -%}
|
| 122 |
+
{{- 'true' if argument else 'false' -}}
|
| 123 |
+
{%- elif argument is mapping -%}
|
| 124 |
+
{{- '{' -}}
|
| 125 |
+
{%- set ns = namespace(found_first=false) -%}
|
| 126 |
+
{%- for key, value in argument | dictsort -%}
|
| 127 |
+
{%- if ns.found_first %},{% endif -%}
|
| 128 |
+
{%- set ns.found_first = true -%}
|
| 129 |
+
{%- if escape_keys -%}
|
| 130 |
+
{{- '<|"|>' + key + '<|"|>' -}}
|
| 131 |
+
{%- else -%}
|
| 132 |
+
{{- key -}}
|
| 133 |
+
{%- endif -%}
|
| 134 |
+
:{{- format_argument(value, escape_keys=escape_keys) -}}
|
| 135 |
+
{%- endfor -%}
|
| 136 |
+
{{- '}' -}}
|
| 137 |
+
{%- elif argument is sequence -%}
|
| 138 |
+
{{- '[' -}}
|
| 139 |
+
{%- for item in argument -%}
|
| 140 |
+
{{- format_argument(item, escape_keys=escape_keys) -}}
|
| 141 |
+
{%- if not loop.last %},{% endif -%}
|
| 142 |
+
{%- endfor -%}
|
| 143 |
+
{{- ']' -}}
|
| 144 |
+
{%- else -%}
|
| 145 |
+
{{- argument -}}
|
| 146 |
+
{%- endif -%}
|
| 147 |
+
{%- endmacro -%}
|
| 148 |
+
{%- macro strip_thinking(text) -%}
|
| 149 |
+
{%- set ns = namespace(result='') -%}
|
| 150 |
+
{%- for part in text.split('<channel|>') -%}
|
| 151 |
+
{%- if '<|channel>' in part -%}
|
| 152 |
+
{%- set ns.result = ns.result + part.split('<|channel>')[0] -%}
|
| 153 |
+
{%- else -%}
|
| 154 |
+
{%- set ns.result = ns.result + part -%}
|
| 155 |
+
{%- endif -%}
|
| 156 |
+
{%- endfor -%}
|
| 157 |
+
{{- ns.result | trim -}}
|
| 158 |
+
{%- endmacro -%}
|
| 159 |
+
|
| 160 |
+
{%- macro format_tool_response_block(tool_name, response) -%}
|
| 161 |
+
{{- '<|tool_response>' -}}
|
| 162 |
+
{%- if response is mapping -%}
|
| 163 |
+
{{- 'response:' + tool_name + '{' -}}
|
| 164 |
+
{%- for key, value in response | dictsort -%}
|
| 165 |
+
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
| 166 |
+
{%- if not loop.last %},{% endif -%}
|
| 167 |
+
{%- endfor -%}
|
| 168 |
+
{{- '}' -}}
|
| 169 |
+
{%- else -%}
|
| 170 |
+
{{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}}
|
| 171 |
+
{%- endif -%}
|
| 172 |
+
{{- '<tool_response|>' -}}
|
| 173 |
+
{%- endmacro -%}
|
| 174 |
+
|
| 175 |
+
{%- set ns = namespace(prev_message_type=None) -%}
|
| 176 |
+
{%- set loop_messages = messages -%}
|
| 177 |
+
{{- bos_token -}}
|
| 178 |
+
{#- Handle System/Tool Definitions Block -#}
|
| 179 |
+
{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}
|
| 180 |
+
{{- '<|turn>system\n' -}}
|
| 181 |
+
{#- Inject Thinking token at the very top of the FIRST system turn -#}
|
| 182 |
+
{%- if enable_thinking is defined and enable_thinking -%}
|
| 183 |
+
{{- '<|think|>\n' -}}
|
| 184 |
+
{%- set ns.prev_message_type = 'think' -%}
|
| 185 |
+
{%- endif -%}
|
| 186 |
+
{%- if messages[0]['role'] in ['system', 'developer'] -%}
|
| 187 |
+
{%- if messages[0]['content'] is string -%}
|
| 188 |
+
{{- messages[0]['content'] | trim -}}
|
| 189 |
+
{%- elif messages[0]['content'] is sequence -%}
|
| 190 |
+
{%- for item in messages[0]['content'] -%}
|
| 191 |
+
{{- item['text'] | trim + ' '-}}
|
| 192 |
+
{%- endfor -%}
|
| 193 |
+
{%- endif -%}
|
| 194 |
+
{%- set loop_messages = messages[1:] -%}
|
| 195 |
+
{%- endif -%}
|
| 196 |
+
{%- if tools -%}
|
| 197 |
+
{%- for tool in tools %}
|
| 198 |
+
{{- '<|tool>' -}}
|
| 199 |
+
{{- format_function_declaration(tool) | trim -}}
|
| 200 |
+
{{- '<tool|>' -}}
|
| 201 |
+
{%- endfor %}
|
| 202 |
+
{%- set ns.prev_message_type = 'tool' -%}
|
| 203 |
+
{%- endif -%}
|
| 204 |
+
{{- '<turn|>\n' -}}
|
| 205 |
+
{%- endif %}
|
| 206 |
+
|
| 207 |
+
{#- Pre-scan: find last user message index for reasoning guard -#}
|
| 208 |
+
{%- set ns_turn = namespace(last_user_idx=-1) -%}
|
| 209 |
+
{%- for i in range(loop_messages | length) -%}
|
| 210 |
+
{%- if loop_messages[i]['role'] == 'user' -%}
|
| 211 |
+
{%- set ns_turn.last_user_idx = i -%}
|
| 212 |
+
{%- endif -%}
|
| 213 |
+
{%- endfor -%}
|
| 214 |
+
|
| 215 |
+
{#- Loop through messages -#}
|
| 216 |
+
{%- for message in loop_messages -%}
|
| 217 |
+
{%- if message['role'] != 'tool' -%}
|
| 218 |
+
{%- set ns.prev_message_type = None -%}
|
| 219 |
+
{%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
|
| 220 |
+
{#- Detect continuation: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}
|
| 221 |
+
{%- set prev_nt = namespace(role=None, found=false) -%}
|
| 222 |
+
{%- if loop.index0 > 0 -%}
|
| 223 |
+
{%- for j in range(loop.index0 - 1, -1, -1) -%}
|
| 224 |
+
{%- if not prev_nt.found -%}
|
| 225 |
+
{%- if loop_messages[j]['role'] != 'tool' -%}
|
| 226 |
+
{%- set prev_nt.role = loop_messages[j]['role'] -%}
|
| 227 |
+
{%- set prev_nt.found = true -%}
|
| 228 |
+
{%- endif -%}
|
| 229 |
+
{%- endif -%}
|
| 230 |
+
{%- endfor -%}
|
| 231 |
+
{%- endif -%}
|
| 232 |
+
{%- set continue_same_model_turn = (role == 'model' and prev_nt.role == 'assistant') -%}
|
| 233 |
+
{%- if not continue_same_model_turn -%}
|
| 234 |
+
{{- '<|turn>' + role + '\n' }}
|
| 235 |
+
{%- endif -%}
|
| 236 |
+
|
| 237 |
+
{#- Render reasoning/reasoning_content as thinking channel -#}
|
| 238 |
+
{%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}
|
| 239 |
+
{%- if thinking_text and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%}
|
| 240 |
+
{{- '<|channel>thought\n' + thinking_text + '\n<channel|>' -}}
|
| 241 |
+
{%- endif -%}
|
| 242 |
+
|
| 243 |
+
{%- if message['tool_calls'] -%}
|
| 244 |
+
{%- for tool_call in message['tool_calls'] -%}
|
| 245 |
+
{%- set function = tool_call['function'] -%}
|
| 246 |
+
{{- '<|tool_call>call:' + function['name'] + '{' -}}
|
| 247 |
+
{%- if function['arguments'] is mapping -%}
|
| 248 |
+
{%- set ns_args = namespace(found_first=false) -%}
|
| 249 |
+
{%- for key, value in function['arguments'] | dictsort -%}
|
| 250 |
+
{%- if ns_args.found_first %},{% endif -%}
|
| 251 |
+
{%- set ns_args.found_first = true -%}
|
| 252 |
+
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
|
| 253 |
+
{%- endfor -%}
|
| 254 |
+
{%- elif function['arguments'] is string -%}
|
| 255 |
+
{{- function['arguments'] -}}
|
| 256 |
+
{%- endif -%}
|
| 257 |
+
{{- '}<tool_call|>' -}}
|
| 258 |
+
{%- endfor -%}
|
| 259 |
+
{%- set ns.prev_message_type = 'tool_call' -%}
|
| 260 |
+
{%- endif -%}
|
| 261 |
+
|
| 262 |
+
{%- set ns_tr_out = namespace(flag=false) -%}
|
| 263 |
+
{%- if message.get('tool_responses') -%}
|
| 264 |
+
{#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}
|
| 265 |
+
{%- for tool_response in message['tool_responses'] -%}
|
| 266 |
+
{{- format_tool_response_block(tool_response['name'] | default('unknown'), tool_response['response']) -}}
|
| 267 |
+
{%- set ns_tr_out.flag = true -%}
|
| 268 |
+
{%- set ns.prev_message_type = 'tool_response' -%}
|
| 269 |
+
{%- endfor -%}
|
| 270 |
+
{%- elif message.get('tool_calls') -%}
|
| 271 |
+
{#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}
|
| 272 |
+
{%- set ns_tool_scan = namespace(stopped=false) -%}
|
| 273 |
+
{%- for k in range(loop.index0 + 1, loop_messages | length) -%}
|
| 274 |
+
{%- if ns_tool_scan.stopped -%}
|
| 275 |
+
{%- elif loop_messages[k]['role'] != 'tool' -%}
|
| 276 |
+
{%- set ns_tool_scan.stopped = true -%}
|
| 277 |
+
{%- else -%}
|
| 278 |
+
{%- set follow = loop_messages[k] -%}
|
| 279 |
+
{#- Resolve tool_call_id to function name -#}
|
| 280 |
+
{%- set ns_tname = namespace(name=follow.get('name') | default('unknown')) -%}
|
| 281 |
+
{%- for tc in message['tool_calls'] -%}
|
| 282 |
+
{%- if tc.get('id') == follow.get('tool_call_id') -%}
|
| 283 |
+
{%- set ns_tname.name = tc['function']['name'] -%}
|
| 284 |
+
{%- endif -%}
|
| 285 |
+
{%- endfor -%}
|
| 286 |
+
{#- Handle content as string or content-parts array -#}
|
| 287 |
+
{%- set tool_body = follow.get('content') -%}
|
| 288 |
+
{%- if tool_body is string -%}
|
| 289 |
+
{{- format_tool_response_block(ns_tname.name, tool_body) -}}
|
| 290 |
+
{%- elif tool_body is sequence and tool_body is not string -%}
|
| 291 |
+
{%- set ns_txt = namespace(s='') -%}
|
| 292 |
+
{%- for part in tool_body -%}
|
| 293 |
+
{%- if part.get('type') == 'text' -%}
|
| 294 |
+
{%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%}
|
| 295 |
+
{%- endif -%}
|
| 296 |
+
{%- endfor -%}
|
| 297 |
+
{{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}
|
| 298 |
+
{%- for part in tool_body -%}
|
| 299 |
+
{%- if part.get('type') == 'image' -%}
|
| 300 |
+
{{- '<|image|>' -}}
|
| 301 |
+
{%- elif part.get('type') == 'audio' -%}
|
| 302 |
+
{{- '<|audio|>' -}}
|
| 303 |
+
{%- elif part.get('type') == 'video' -%}
|
| 304 |
+
{{- '<|video|>' -}}
|
| 305 |
+
{%- endif -%}
|
| 306 |
+
{%- endfor -%}
|
| 307 |
+
{%- else -%}
|
| 308 |
+
{{- format_tool_response_block(ns_tname.name, tool_body) -}}
|
| 309 |
+
{%- endif -%}
|
| 310 |
+
{%- set ns_tr_out.flag = true -%}
|
| 311 |
+
{%- set ns.prev_message_type = 'tool_response' -%}
|
| 312 |
+
{%- endif -%}
|
| 313 |
+
{%- endfor -%}
|
| 314 |
+
{%- endif -%}
|
| 315 |
+
|
| 316 |
+
{%- set captured_content -%}
|
| 317 |
+
{%- if message['content'] is string -%}
|
| 318 |
+
{%- if role == 'model' -%}
|
| 319 |
+
{{- strip_thinking(message['content']) -}}
|
| 320 |
+
{%- else -%}
|
| 321 |
+
{{- message['content'] | trim -}}
|
| 322 |
+
{%- endif -%}
|
| 323 |
+
{%- elif message['content'] is sequence -%}
|
| 324 |
+
{%- for item in message['content'] -%}
|
| 325 |
+
{%- if item['type'] == 'text' -%}
|
| 326 |
+
{%- if role == 'model' -%}
|
| 327 |
+
{{- strip_thinking(item['text']) -}}
|
| 328 |
+
{%- else -%}
|
| 329 |
+
{{- item['text'] | trim -}}
|
| 330 |
+
{%- endif -%}
|
| 331 |
+
{%- elif item['type'] == 'image' -%}
|
| 332 |
+
{{- '<|image|>' -}}
|
| 333 |
+
{%- set ns.prev_message_type = 'image' -%}
|
| 334 |
+
{%- elif item['type'] == 'audio' -%}
|
| 335 |
+
{{- '<|audio|>' -}}
|
| 336 |
+
{%- set ns.prev_message_type = 'audio' -%}
|
| 337 |
+
{%- elif item['type'] == 'video' -%}
|
| 338 |
+
{{- '<|video|>' -}}
|
| 339 |
+
{%- set ns.prev_message_type = 'video' -%}
|
| 340 |
+
{%- endif -%}
|
| 341 |
+
{%- endfor -%}
|
| 342 |
+
{%- endif -%}
|
| 343 |
+
{%- endset -%}
|
| 344 |
+
|
| 345 |
+
{{- captured_content -}}
|
| 346 |
+
{%- set has_content = captured_content | trim | length > 0 -%}
|
| 347 |
+
|
| 348 |
+
{%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}
|
| 349 |
+
{{- '<|tool_response>' -}}
|
| 350 |
+
{%- elif not (ns_tr_out.flag and not has_content) -%}
|
| 351 |
+
{{- '<turn|>\n' -}}
|
| 352 |
+
{%- endif -%}
|
| 353 |
+
{%- endif -%}
|
| 354 |
+
{%- endfor -%}
|
| 355 |
+
|
| 356 |
+
{%- if add_generation_prompt -%}
|
| 357 |
+
{%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}
|
| 358 |
+
{{- '<|turn>model\n' -}}
|
| 359 |
+
{%- endif -%}
|
| 360 |
+
{%- endif -%}
|
config.json
ADDED
|
@@ -0,0 +1,197 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Gemma4ForConditionalGeneration"
|
| 4 |
+
],
|
| 5 |
+
"audio_config": {
|
| 6 |
+
"_name_or_path": "",
|
| 7 |
+
"architectures": null,
|
| 8 |
+
"attention_chunk_size": 12,
|
| 9 |
+
"attention_context_left": 13,
|
| 10 |
+
"attention_context_right": 0,
|
| 11 |
+
"attention_invalid_logits_value": -1000000000.0,
|
| 12 |
+
"attention_logit_cap": 50.0,
|
| 13 |
+
"chunk_size_feed_forward": 0,
|
| 14 |
+
"conv_kernel_size": 5,
|
| 15 |
+
"dtype": "bfloat16",
|
| 16 |
+
"gradient_clipping": 10000000000.0,
|
| 17 |
+
"hidden_act": "silu",
|
| 18 |
+
"hidden_size": 1024,
|
| 19 |
+
"id2label": {
|
| 20 |
+
"0": "LABEL_0",
|
| 21 |
+
"1": "LABEL_1"
|
| 22 |
+
},
|
| 23 |
+
"initializer_range": 0.02,
|
| 24 |
+
"is_encoder_decoder": false,
|
| 25 |
+
"label2id": {
|
| 26 |
+
"LABEL_0": 0,
|
| 27 |
+
"LABEL_1": 1
|
| 28 |
+
},
|
| 29 |
+
"model_type": "gemma4_audio",
|
| 30 |
+
"num_attention_heads": 8,
|
| 31 |
+
"num_hidden_layers": 12,
|
| 32 |
+
"output_attentions": false,
|
| 33 |
+
"output_hidden_states": false,
|
| 34 |
+
"output_proj_dims": 1536,
|
| 35 |
+
"problem_type": null,
|
| 36 |
+
"residual_weight": 0.5,
|
| 37 |
+
"return_dict": true,
|
| 38 |
+
"rms_norm_eps": 1e-06,
|
| 39 |
+
"subsampling_conv_channels": [
|
| 40 |
+
128,
|
| 41 |
+
32
|
| 42 |
+
],
|
| 43 |
+
"use_clipped_linears": true
|
| 44 |
+
},
|
| 45 |
+
"audio_token_id": 258881,
|
| 46 |
+
"boa_token_id": 256000,
|
| 47 |
+
"boi_token_id": 255999,
|
| 48 |
+
"dtype": "bfloat16",
|
| 49 |
+
"eoa_token_id": 258883,
|
| 50 |
+
"eoa_token_index": 258883,
|
| 51 |
+
"eoi_token_id": 258882,
|
| 52 |
+
"eos_token_id": [
|
| 53 |
+
1,
|
| 54 |
+
106
|
| 55 |
+
],
|
| 56 |
+
"image_token_id": 258880,
|
| 57 |
+
"initializer_range": 0.02,
|
| 58 |
+
"model_type": "gemma4",
|
| 59 |
+
"text_config": {
|
| 60 |
+
"attention_bias": false,
|
| 61 |
+
"attention_dropout": 0.0,
|
| 62 |
+
"attention_k_eq_v": false,
|
| 63 |
+
"bos_token_id": 2,
|
| 64 |
+
"dtype": "bfloat16",
|
| 65 |
+
"enable_moe_block": false,
|
| 66 |
+
"eos_token_id": 1,
|
| 67 |
+
"expert_intermediate_size": null,
|
| 68 |
+
"final_logit_softcapping": 30.0,
|
| 69 |
+
"global_head_dim": 512,
|
| 70 |
+
"head_dim": 256,
|
| 71 |
+
"hidden_activation": "gelu_pytorch_tanh",
|
| 72 |
+
"hidden_size": 2560,
|
| 73 |
+
"hidden_size_per_layer_input": 256,
|
| 74 |
+
"initializer_range": 0.02,
|
| 75 |
+
"intermediate_size": 10240,
|
| 76 |
+
"layer_types": [
|
| 77 |
+
"sliding_attention",
|
| 78 |
+
"sliding_attention",
|
| 79 |
+
"sliding_attention",
|
| 80 |
+
"sliding_attention",
|
| 81 |
+
"sliding_attention",
|
| 82 |
+
"full_attention",
|
| 83 |
+
"sliding_attention",
|
| 84 |
+
"sliding_attention",
|
| 85 |
+
"sliding_attention",
|
| 86 |
+
"sliding_attention",
|
| 87 |
+
"sliding_attention",
|
| 88 |
+
"full_attention",
|
| 89 |
+
"sliding_attention",
|
| 90 |
+
"sliding_attention",
|
| 91 |
+
"sliding_attention",
|
| 92 |
+
"sliding_attention",
|
| 93 |
+
"sliding_attention",
|
| 94 |
+
"full_attention",
|
| 95 |
+
"sliding_attention",
|
| 96 |
+
"sliding_attention",
|
| 97 |
+
"sliding_attention",
|
| 98 |
+
"sliding_attention",
|
| 99 |
+
"sliding_attention",
|
| 100 |
+
"full_attention",
|
| 101 |
+
"sliding_attention",
|
| 102 |
+
"sliding_attention",
|
| 103 |
+
"sliding_attention",
|
| 104 |
+
"sliding_attention",
|
| 105 |
+
"sliding_attention",
|
| 106 |
+
"full_attention",
|
| 107 |
+
"sliding_attention",
|
| 108 |
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|
| 109 |
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"sliding_attention",
|
| 110 |
+
"sliding_attention",
|
| 111 |
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"sliding_attention",
|
| 112 |
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"full_attention",
|
| 113 |
+
"sliding_attention",
|
| 114 |
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"sliding_attention",
|
| 115 |
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"sliding_attention",
|
| 116 |
+
"sliding_attention",
|
| 117 |
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"sliding_attention",
|
| 118 |
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"full_attention"
|
| 119 |
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],
|
| 120 |
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"max_position_embeddings": 131072,
|
| 121 |
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|
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|
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| 130 |
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|
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|
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|
| 135 |
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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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|
| 149 |
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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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|
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| 191 |
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|
| 192 |
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|
| 195 |
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},
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| 196 |
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|
| 197 |
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}
|
generation_config.json
ADDED
|
@@ -0,0 +1,14 @@
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|
| 1 |
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{
|
| 2 |
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"bos_token_id": 2,
|
| 3 |
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"do_sample": true,
|
| 4 |
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"eos_token_id": [
|
| 5 |
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1,
|
| 6 |
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106,
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| 7 |
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50
|
| 8 |
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| 9 |
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| 10 |
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|
| 11 |
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|
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|
| 13 |
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"transformers_version": "5.5.0.dev0"
|
| 14 |
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|
merge_manifest.json
ADDED
|
@@ -0,0 +1,11 @@
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|
|
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|
|
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|
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|
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|
|
| 1 |
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{
|
| 2 |
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"output": "D:\\250m-train\\outputs\\gemma4_e4b_lime_persona500_merged_hf\\model.safetensors",
|
| 3 |
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"tensors": 2130,
|
| 4 |
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"merged_lora_tensors": 258,
|
| 5 |
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"bytes": 15992595908,
|
| 6 |
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"base_dir": "D:\\250m-train\\gemma-4-E4B-it",
|
| 7 |
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"adapter_dir": "D:\\250m-train\\라임 백업_\\프로토타입\\checkpoints\\gemma4_e4b_lime_lora_persona500",
|
| 8 |
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"alpha": 32.0,
|
| 9 |
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"rank": 16,
|
| 10 |
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"scale": 2.0
|
| 11 |
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}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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|
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|
|
|
|
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 15992595908
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processor_config.json
ADDED
|
@@ -0,0 +1,75 @@
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|
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|
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|
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|
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|
| 1 |
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{
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| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
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|
| 17 |
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| 19 |
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| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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},
|
| 25 |
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"image_processor": {
|
| 26 |
+
"do_convert_rgb": true,
|
| 27 |
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"do_normalize": false,
|
| 28 |
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"do_rescale": true,
|
| 29 |
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"do_resize": true,
|
| 30 |
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"image_mean": [
|
| 31 |
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0.0,
|
| 32 |
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0.0,
|
| 33 |
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0.0
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],
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| 35 |
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"image_processor_type": "Gemma4ImageProcessor",
|
| 36 |
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|
| 37 |
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1.0,
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| 42 |
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|
| 43 |
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| 53 |
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tokenizer.json
ADDED
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size 32169626
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tokenizer_config.json
ADDED
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@@ -0,0 +1,95 @@
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| 4 |
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| 58 |
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| 59 |
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| 66 |
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| 67 |
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"name": {
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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"type": "object",
|
| 72 |
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"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
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| 73 |
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| 74 |
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"type": {
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| 75 |
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"const": "function"
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| 76 |
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|
| 77 |
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|
| 78 |
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"type": "object"
|
| 79 |
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| 80 |
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"type": "array",
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| 81 |
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| 82 |
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"type": "object",
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| 85 |
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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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| 86 |
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},
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| 91 |
+
"str_token": "<|tool_response>",
|
| 92 |
+
"think_token": "<|think|>",
|
| 93 |
+
"tokenizer_class": "GemmaTokenizer",
|
| 94 |
+
"unk_token": "<unk>"
|
| 95 |
+
}
|