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Model: trillionlabs/Tri-1.8B-Translation Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- translation
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- multilingual
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- trillionlabs
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- preview
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license: apache-2.0
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language:
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- ko
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- en
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- zh
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- ja
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---
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# Tri-1.8B Translation
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We release **Tri-1.8B Translation**, a lightweight multilingual translation model from **Trillion Labs**.
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Tri-1.8B Translate is trained through pretraining and supervised fine-tuning (SFT), and was distilled from our larger Tri-21B model to preserve strong translation quality in a much smaller, deployment-friendly 1.8B parameter model. It supports all translation directions among English, Korean, Japanese, and Chinese.
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## ✨ Highlights
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* **Compact & efficient:** \~1.8B params, easy to serve on a single GPU.
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* **Multilingual:** Fully bidirectional **EN ↔ KO ↔ JA ↔ ZH**.
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* **Simple prompts:** Works with a short **task instruction + `<lang>` tag**.
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* **Research-ready:** Suitable for domain SFT or lightweight adapters.
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---
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## 🧾 Prompt format
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```
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Translate the following {SRC_NAME} text into {TGT_NAME}:
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{TEXT} <{lang_tag}>
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```
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Where `{lang_tag} ∈ { en, ko, ja, zh }`.
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---
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## 🔧 Usage
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### 1) 🤗 Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "trillionlabs/Tri-1.8B-Translation"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
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text = "안녕하세요"
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messages = [
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{"role": "user", "content": f"Translate the following Korean text into English:\n{text} <en>"}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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return_tensors="pt",
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add_generation_prompt=True
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=256,
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do_sample=False,
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pad_token_id=tokenizer.eos_token_id
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)
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full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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translation = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
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print(f"Korean: {text}")
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print(f"English: {translation}")
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```
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---
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### 2) Local vLLM
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="trillionlabs/Tri-1.8B-Translation")
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sp = SamplingParams(temperature=0.3, max_tokens=512)
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def translate(text, target="en"):
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prompt = f"Translate into {target}:\n{text} <{target}>"
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out = llm.chat([{"role": "user", "content": prompt}], sampling_params=sp)
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return out[0].outputs[0].text.strip()
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print(translate("안녕하세요", "en"))
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```
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---
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### 3) API client (OpenAI-compatible)
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```python
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import openai
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client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
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def translate(text, target="en"):
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prompt = f"Translate into {target}:\n{text} <{target}>"
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resp = client.chat.completions.create(
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model="trillionlabs/Tri-1.8B-Translation",
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messages=[{"role": "user", "content": prompt}],
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temperature=0.3,
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max_tokens=512,
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)
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return resp.choices[0].message.content.strip()
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print(translate("안녕하세요", "en"))
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```
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## 📜 License
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Apache-2.0 (for model weights & code). Please verify data licenses for your use.
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## 🙏 Acknowledgments
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* Thanks to the **ByteDance Seed team** for releasing **Seed-X**; our prompt template and some training design were adapted from their paper.
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## 📚 Citation
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If you use **Tri-1.8B Translation**, please cite:
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```bibtex
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@misc{suk2025tri18b,
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title = {Tri-1.8B Translation: A Lightweight Multilingual Translation Model},
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author = {Juyoung Suk and Trillion Labs},
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year = {2025},
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howpublished = {\url{https://huggingface.co/trillionlabs/Tri-1.8B-Translation}}
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}
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```
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chat_template.jinja
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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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"head_dim": 128,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 25,
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"num_key_value_heads": 16,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.53.3",
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"use_cache": false,
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"vocab_size": 128129
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}
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|
||||
}
|
||||
153
special_tokens_map.json
Normal file
153
special_tokens_map.json
Normal file
@@ -0,0 +1,153 @@
|
||||
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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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||||
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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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||||
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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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||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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|
||||
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|
||||
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|
||||
"<|reserved_token_120|>",
|
||||
"<|reserved_token_121|>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|pad_token|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
640362
tokenizer.json
Normal file
640362
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
1175
tokenizer_config.json
Normal file
1175
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user