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Model: k08200/gaon-1.7b-v2-instruct Source: Original Platform
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README.md
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README.md
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---
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language:
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- ko
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- korean
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- from-scratch
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- qwen3-architecture
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base_model:
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- Qwen/Qwen3-1.7B
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---
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# Gaon-1.7B v2 Instruct (가온)
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A bilingual (Korean + English) 1.7B chat model **trained entirely from scratch** —
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architecture, data pipeline, pretraining, and instruction tuning — by one person on
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borrowed idle GPUs, for $0. *Gaon* is pure Korean for "center/core."
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- **Architecture:** Qwen3-1.7B-compatible (28L, hidden 2048, GQA 16Q/8KV, QK-Norm,
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SwiGLU, tied embeddings, vocab 151,936). Loads with `Qwen3ForCausalLM`.
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- **Pretraining:** 34B tokens (FineWeb-Edu EN + FineWeb-2 KO + ~20% Python code),
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4× B200 FSDP, final loss 1.96.
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- **Instruction tuning:** sequence-level distillation from Qwen2.5-7B-Instruct
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(Apache-2.0), ~9k KO/EN instructions, SFT loss 1.26.
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- **Code & full tech report:** https://github.com/k08200/gaon
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**Run locally:** `ollama run hf.co/k08200/gaon-1.7b-v2-instruct-GGUF` — or community
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GGUF quants (12 sizes, 516 MB – 3.4 GB, by [@mradermacher](https://huggingface.co/mradermacher)):
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[static](https://huggingface.co/mradermacher/gaon-1.7b-v2-instruct-GGUF) ·
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[imatrix](https://huggingface.co/mradermacher/gaon-1.7b-v2-instruct-i1-GGUF)
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**Live demo:** [KO↔EN translator in your browser (WebGPU)](https://k08200.github.io/gaon/demo/)
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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m = "k08200/gaon-1.7b-v2-instruct"
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tok = AutoTokenizer.from_pretrained(m)
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model = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.bfloat16).eval()
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msgs = [{"role": "user", "content": "한국의 수도는 어디인가요?"}]
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prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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enc = tok(prompt, return_tensors="pt")
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out = model.generate(**enc, max_new_tokens=200, do_sample=True, temperature=0.7)
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print(tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True))
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```
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## Benchmarks
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Base model (v2), lm-eval-harness, 0-shot, same settings for both:
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| Benchmark | Gaon-1.7B v2 | Qwen3-1.7B-Base | Random |
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|---|---|---|---|
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| MMLU (English knowledge) | 25.1 | 62.6 | 25.0 |
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| KMMLU (Korean knowledge) | 22.3 | 35.5 | 25.0 |
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| HAERAE (Korean culture/lexis) | 19.9 | 46.8 | ~20 |
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| KoBEST (Korean understanding) | 51.5 | — | ~50 |
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This is the most honest number in the project: Gaon is **fluent in two languages,
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follows instructions, and translates**, yet scores at *chance* on knowledge — because
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Qwen3-1.7B (same architecture, same size) trained on ~36T tokens, roughly 1000× our
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34B. **Linguistic competence emerges in tens of billions of tokens; world knowledge
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needs trillions.** A continued-pretraining run (96B Korean-heavy tokens) lifts KMMLU
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to ~27–29 then plateaus — see the [tech report](https://github.com/k08200/gaon/blob/main/docs/TECH_REPORT.md) §6.
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## Honest limitations
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A **from-scratch credential and research artifact**, not a frontier competitor.
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Knowledge benchmarks at chance level (above); coding and strict-format tasks are weak.
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Korean/English chat, explanation, and simple translation work well. Best used as a
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reproducible small-LLM pipeline and a base for vertical fine-tuning on domain data.
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89
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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63
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "float32",
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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],
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"max_position_embeddings": 2048,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.12.1",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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{
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"_from_model_config": true,
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"eos_token_id": [
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151645
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"output_attentions": false,
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"output_hidden_states": false,
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"transformers_version": "5.12.1",
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"use_cache": true
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 6882335328
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3
tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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