初始化项目，由ModelHub XC社区提供模型
Model: Ismantic/Interpreter-Qwen3-1.7B Source: Original Platform
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.gitattributes
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98
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-1.7B-Base
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language:
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- zh
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- en
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pipeline_tag: translation
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library_name: transformers
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tags:
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- translation
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- qwen3
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- zh-en
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- en-zh
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- sft
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- cpo
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- grpo
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datasets:
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- haoranxu/ALMA-Human-Parallel
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- haoranxu/X-ALMA-Parallel-Data
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---
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# Interpreter-Qwen3-1.7B
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A 1.7B Chinese↔English translation model, trained **SFT → CPO → GRPO**.
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This model is one half of an A/B pair. The two differ in **exactly two things** — the base checkpoint and the tokenizer — and share the same data, hyperparameters, losses and rewards. The other half is [`Ismantic/Interpreter-Qwen3-1.7B-ReTok`](https://huggingface.co/Ismantic/Interpreter-Qwen3-1.7B-ReTok).
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| | this model |
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|---|---|
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| Tokenizer | Qwen3 native BBPE (151643) |
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| Chat format | ChatML |
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| Pipeline | SFT → CPO → GRPO |
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## Results (WMT23)
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| direction | BLEU / COMET |
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|---|---|
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| zh→en | 20.31 / 0.8053 |
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| en→zh | 33.69 / 0.8540 |
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### How to read these numbers
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WMT23 is the primary test set — **WMT22 is excluded**, roughly 17.5% of it leaked
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into the ALMA SFT data. COMET is `Unbabel/wmt22-comet-da`, which is *also* the
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GRPO reward, so gains were cross-checked on WMT23/24 + Flores-200 with BLEU and
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chrF to rule out reward hacking.
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vLLM greedy decoding is not bit-reproducible; BLEU varies by ~0.1 between runs.
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Differences at that scale are noise.
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## Usage
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Standard ChatML with a fixed translation instruction; `<|im_end|>` is the stop
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token. Greedy decoding recommended.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("Ismantic/Interpreter-Qwen3-1.7B")
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model = AutoModelForCausalLM.from_pretrained("Ismantic/Interpreter-Qwen3-1.7B", torch_dtype=torch.bfloat16).cuda().eval()
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instr = ("Translate the following text from Chinese to English.\n"
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"Chinese: 人工智能正在深刻改变我们的生活方式。\nEnglish:")
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prompt = f"<|im_start|>user\n{instr}<|im_end|>\n<|im_start|>assistant\n"
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ids = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**ids, max_new_tokens=256, do_sample=False,
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eos_token_id=tok.convert_tokens_to_ids("<|im_end|>"))
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print(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True).strip())
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```
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vLLM works too — same ChatML prompt, `stop=["<|im_end|>"]`.
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For a quick interactive demo, grab `translate.py` from this repo and run
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`python translate.py` (needs `vllm`) — type sentences, zh↔en auto-detected.
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## Training
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Three stages, all on a single RTX 4090:
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- **SFT** — full fine-tune on ~36.8K zh↔en pairs (ALMA + X-ALMA human parallel,
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WMT22/23 leakage removed), loss on the response only.
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- **CPO** — LoRA preference training, `-log σ(β·(logπ_w − logπ_l)) + λ·NLL(y_w)`,
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on ~44K **self-generated** preference pairs (5 candidates sampled from the SFT
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model, best/worst picked by COMET). LoRA only — full-parameter CPO collapses
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the model.
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- **GRPO** — full-parameter RL. Reward = reference-based `wmt22-comet-da` COMET
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(1.0) + a 4-gram repetition penalty (0.3), over WMT17–21 source prompts.
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Training code, data provenance and the negative results (what was tried and
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rejected, with numbers) are in the
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[Interpreter repo](https://github.com/Ismantic/Interpreter).
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## License & attribution
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Apache-2.0, following the base model. Training data derives from the ALMA /
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X-ALMA parallel corpora and WMT news test sets. Please respect the licenses of
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those upstream models and datasets.
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28
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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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 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.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
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{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
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{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').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 %}
|
||||
{%- 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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{{- message.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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60
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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"dtype": "bfloat16",
|
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"eos_token_id": 151645,
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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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"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",
|
||||
"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",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
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"max_position_embeddings": 32768,
|
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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,
|
||||
"num_key_value_heads": 8,
|
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"pad_token_id": 151643,
|
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"rms_norm_eps": 1e-06,
|
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"rope_scaling": null,
|
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"rope_theta": 1000000,
|
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"sliding_window": null,
|
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"tie_word_embeddings": true,
|
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"transformers_version": "4.57.6",
|
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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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8
generation_config.json
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generation_config.json
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{
|
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"eos_token_id": [
|
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151645
|
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],
|
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"max_new_tokens": 2048,
|
||||
"pad_token_id": 151643,
|
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"transformers_version": "4.57.6"
|
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}
|
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151388
merges.txt
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151388
merges.txt
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Load Diff
3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:002140f270a46b484d7ff2079b6256b055c422b83e141e452b4248ee31c95957
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size 3441185608
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25
special_tokens_map.json
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special_tokens_map.json
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{
|
||||
"additional_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_start|>",
|
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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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"eos_token": "<|im_end|>",
|
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"pad_token": {
|
||||
"content": "<|endoftext|>",
|
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"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
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BIN
tokenizer.json
(Stored with Git LFS)
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BIN
tokenizer.json
(Stored with Git LFS)
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239
tokenizer_config.json
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tokenizer_config.json
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{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
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"added_tokens_decoder": {
|
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"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false,
|
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"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
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},
|
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"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
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"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
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},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
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"normalized": false,
|
||||
"rstrip": false,
|
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"single_word": false,
|
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"special": true
|
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},
|
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"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
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"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
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"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|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,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
47
translate.py
Normal file
47
translate.py
Normal file
@@ -0,0 +1,47 @@
|
||||
"""Interactive zh<->en translation demo for Interpreter-Qwen3-1.7B.
|
||||
|
||||
pip install vllm
|
||||
python translate.py # loads Ismantic/Interpreter-Qwen3-1.7B
|
||||
python translate.py --model_path ./ # or a local snapshot dir
|
||||
|
||||
Type a sentence + Enter; direction (zh->en / en->zh) is auto-detected by whether the
|
||||
line contains Chinese characters. Ctrl-C / Ctrl-D to quit. Uses the model's ChatML
|
||||
prompt format with <|im_end|> as the stop token and greedy decoding.
|
||||
"""
|
||||
import sys
|
||||
import argparse
|
||||
from vllm import LLM, SamplingParams
|
||||
|
||||
PROMPT_ZH2EN = "Translate the following text from Chinese to English.\nChinese: {src}\nEnglish:"
|
||||
PROMPT_EN2ZH = "Translate the following text from English to Chinese.\nEnglish: {src}\nChinese:"
|
||||
|
||||
|
||||
def is_zh(s):
|
||||
return any("一" <= c <= "鿿" for c in s)
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--model_path", default="Ismantic/Interpreter-Qwen3-1.7B")
|
||||
ap.add_argument("--gpu_mem", type=float, default=0.5)
|
||||
args = ap.parse_args()
|
||||
|
||||
llm = LLM(model=args.model_path, gpu_memory_utilization=args.gpu_mem, max_model_len=1024)
|
||||
sp = SamplingParams(max_tokens=256, temperature=0, stop=["<|im_end|>"])
|
||||
|
||||
print("\n>>> Type a sentence and press Enter (zh<->en auto-detected). Ctrl-C / Ctrl-D to quit.\n")
|
||||
try:
|
||||
for line in sys.stdin:
|
||||
src = line.strip()
|
||||
if not src:
|
||||
continue
|
||||
tmpl = PROMPT_ZH2EN if is_zh(src) else PROMPT_EN2ZH
|
||||
prompt = f"<|im_start|>user\n{tmpl.format(src=src)}<|im_end|>\n<|im_start|>assistant\n"
|
||||
out = llm.generate([prompt], sp, use_tqdm=False)[0].outputs[0].text.strip()
|
||||
print("->", out, "\n")
|
||||
except (KeyboardInterrupt, EOFError):
|
||||
print("\nbye")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user