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Model: 3tic/Orion-Qwen3-1.7B-SFT-v2603
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---
license: apache-2.0
language:
- ja
- zh
base_model:
- 3tic/Orion-Qwen3-1.7B-CPT-v2603
pipeline_tag: text-generation
tags:
- lightnovel
---
# Orion-Qwen3-1.7B-SFT-v2603
基于 [Orion-Qwen3-1.7B-CPT-v2603](https://huggingface.co/3tic/Orion-Qwen3-1.7B-CPT-v2603) 在轻小说、游戏、动漫文本数据上训练的翻译模型,支持术语表。
* 本版模型优化了术语表使用和上下文关系处理
## Prompt 结构
```
[上文] ← 可选,每 chunk 不同
[术语表] ← 可选,同文档共享,仅包含当前 chunk 命中的术语
[提示词] ← 根据有无上文/术语表选择措辞
[JSONL 内容] ← 待翻译文本
```
---
## 模板 1: plain无术语表无上文
### user
```
将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果不要额外解释
{"1":"原文1"}
{"2":"原文2"}
```
### assistant
```
{"1":"译文1"}
{"2":"译文2"}
```
### 示例
**user:**
```
将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果不要额外解释
{"1":"まだなにか、話すべきことがあっただろうか。"}
```
**assistant:**
```
{"1":"还有什么,需要说的事情吗。"}
```
---
## 模板 2: glossary有术语表无上文
### user
```
术语表:
実体A→译名A
実体B→译名B
参考术语表中的译法将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果
{"1":"原文1"}
{"2":"原文2"}
```
### assistant
```
{"1":"译文1"}
{"2":"译文2"}
```
### 示例
**user:**
```
术语表:
クトゥリフ→库托莉芙
サーフェちゃん→莎妃
ライム→莉姆
参考术语表中的译法将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果
{"1":"「思い出話をしましょう、と言っているのよ。私もグレンとは話せないし」"}
{"2":"うにょん! と楕円形に広がっていたライムが、あっという間に人の形に戻った。"}
{"3":"「いいんデスか! サーフェちゃん」"}
```
---
## 模板 3: context无术语表有上文
### user
```
上文行1
上文行2
上文行3
参考上文信息将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果
{"1":"原文1"}
{"2":"原文2"}
```
### assistant
```
{"1":"译文1"}
{"2":"译文2"}
```
### 示例
**user:**
```
「え……だっテ、グレンくんは教室でも浮いてましたシ……みんなと仲良くなれるきっかけになればと思ったんデス……」
サーフェとしては。
ライムがやったことは、サーフェの考えと似てはいても、もう少し先を見ていた。
参考上文信息将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果
{"1":"「医学部学生ではあるけど、身体の悩みを聞きます……というのは評判になりましたね」"}
{"2":"「エヘヘ! 噂を流した甲斐がありましター!」"}
{"3":"「それも貴女の仕業だったのね」"}
```
---
## 模板 4: glossary_context有术语表有上文
### user
```
上文行1
上文行2
上文行3
术语表:
実体A→译名A
実体B→译名B
参考上文和术语表将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果
{"1":"原文1"}
{"2":"原文2"}
```
### assistant
```
{"1":"译文1"}
{"2":"译文2"}
```
### 示例
**user:**
```
アカデミーは多数の学部に分かれ、大陸各地から有能な学者が招聘された。
ある日、アカデミーに一人の少年が留学した。
新入生の名はグレン・リトバイト。
术语表:
クトゥリフ→库托莉芙
グレン・リトバイト→格伦·利特贝特
ネメア→涅米亚
ネメア・アカデミー→涅米亚·学院
参考上文和术语表将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果
{"1":"ネメア・アカデミー。"}
{"2":"魔族領首都ネメア。その大都市が誇る、最大の教育機関。"}
{"3":"名前はグレン。"}
{"4":"手にしているのは、教授であるクトゥリフからの通知である。"}
```
---
## Transformers 推理示例
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "3tic/Orion-Qwen3-1.7B-CPT-v2603"
tokenizer = AutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True,
padding_side="left",
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
device_map="auto",
)
prompt = (
"将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果不要额外解释\n"
'{"1": "おはようございます"}\n'
)
text = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=128,
do_sample=False,
temperature=1.0,
pad_token_id=tokenizer.pad_token_id,
)
generated_ids = outputs[0][inputs["input_ids"].shape[-1]:]
response = tokenizer.decode(generated_ids, skip_special_tokens=True)
print(response)
```
## vLLM 推理示例
```python
from vllm import LLM, SamplingParams
model_path = "3tic/Orion-Qwen3-1.7B-CPT-v2603"
llm = LLM(model=model_path, tensor_parallel_size=1, max_model_len=1024)
prompt = (
"将以下文本翻译为简体中文使用JSONLINE格式输出翻译结果只需输出翻译结果不要额外解释\n"
'{"1": "おはようございます"}\n'
)
outputs = llm.chat(
messages=[[{"role": "user", "content": prompt}]],
sampling_params=SamplingParams(max_tokens=128, temperature=0.0),
)
print(outputs[0].outputs[0].text)
```

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# 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>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\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" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set content = message.content %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in message.content %}
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "bfloat16",
"eos_token_id": 151643,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
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"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.2.0",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": false,
"eos_token_id": [
151643,
151645
],
"max_new_tokens": 2048,
"temperature": 1.0,
"top_k": 50,
"top_p": 1.0,
"transformers_version": "5.2.0"
}

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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
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"lstrip": false,
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"single_word": false,
"special": true
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"151653": {
"content": "<|vision_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
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"151654": {
"content": "<|vision_pad|>",
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"content": "<|image_pad|>",
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"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
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"151657": {
"content": "<tool_call>",
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},
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"content": "</tool_call>",
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"special": false
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"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
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"single_word": false,
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"151660": {
"content": "<|fim_middle|>",
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"151661": {
"content": "<|fim_suffix|>",
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"151662": {
"content": "<|fim_pad|>",
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"151664": {
"content": "<|file_sep|>",
"lstrip": false,
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"rstrip": false,
"single_word": false,
"special": false
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"151665": {
"content": "<tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
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"special": false
},
"151666": {
"content": "</tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
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"151667": {
"content": "<think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
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},
"151668": {
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"special": false
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"additional_special_tokens": [
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"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"errors": "replace",
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

1
vocab.json Normal file

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