初始化项目，由ModelHub XC社区提供模型
Model: Madras1/Jade-20B 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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- pt
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- en
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license: apache-2.0
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base_model:
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- unsloth/gpt-oss-20b
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- openai/gpt-oss-20b
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base_model_relation: finetune
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- pt-br
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- portuguese
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- brazilian-portuguese
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- conversational
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- chatbot
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- persona
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- unsloth
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- 4-bit
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- bitsandbytes
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- qwen3
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---
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# Jade-20b
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Jade-20b is a Brazilian Portuguese conversational finetune of gpt-oss-20b built to express a strong, persistent persona. This model is designed for PT-BR chat, chatbot use cases, and character-style interaction, with colloquial language, abbreviations, slang, and a WhatsApp-like tone.
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## Model Summary
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Jade-20b is a persona-first model. It was intentionally finetuned so the model speaks like **Jade** even without a strong `system prompt`. Because of that, the model often answers in PT-BR with informal phrasing such as `vc`, slang, and a friendly conversational tone from the very first turn.
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## Model Details
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- Developed by: `Madras1`
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- Base model: `unsloth/gpt-oss-20b`
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- Model type: conversational text-generation finetune
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- Primary language: Brazilian Portuguese (`pt-BR`)
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- License: `apache-2.0`
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## Intended Behavior
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This model was trained to:
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- speak naturally in Brazilian Portuguese
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- maintain a consistent Jade persona
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- sound informal, friendly, and chat-oriented
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- work well in casual assistant and conversational use cases
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Typical behavior includes:
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- abbreviations like `vc`
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- light slang and colloquial wording
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- short expressions such as `tmj`, `mano`, `tlgd`
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- a more human and less robotic tone
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If Jade already sounds like a recurring character during inference, that is expected behavior, not an error.
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## Training Intent
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The finetune objective was to make the persona live in the **weights**, not only in prompting.
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High-level training approach:
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- synthetic PT-BR prompt generation for chat-like situations
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- persona-driven response distillation
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- supervised finetuning on conversational data
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- removal of `system` persona instructions during SFT so the model directly internalizes the Jade style
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This is why the model can already answer with personality, abbreviations, and slang even with a simple user-only prompt.
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## Training Setup
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High-level setup used for this finetune:
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- around `25,000` examples
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- `3` epochs
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- Unsloth-based SFT pipeline
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- chat-style data in Portuguese
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## Recommended Use
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Best fit:
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- PT-BR chat assistants
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- persona bots
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- WhatsApp-style conversational agents
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- lightweight entertainment or social AI experiences
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Less ideal for:
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- formal writing
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- highly neutral assistant behavior
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- high-stakes legal, medical, or financial contexts
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## Prompting Tips
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For the strongest Jade behavior:
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- use a simple user message
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- avoid a formal system prompt that fights the finetune
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- keep prompts conversational when possible
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Example prompts:
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- `oi jade, tudo bem?`
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- `jade, me explica isso de um jeito simples`
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- `vc acha que vale a pena estudar python hoje?`
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## Example Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Madras1/Jade-20b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "oi jade, tudo bem?"}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(text, return_tensors="pt").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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temperature=0.7,
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top_p=0.9,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Limitations
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Because this is a persona-oriented finetune:
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- it may sound informal in contexts where a neutral tone would be better
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- it may over-index on chat style depending on the prompt
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- it is optimized more for persona consistency than strict formality
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## Links
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https://github.com/MadrasLe/JadeLLMV-1
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chat_template.jinja
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{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user
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' + message['content'] + '<|im_end|>
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'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant
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' + message['content'] + '<|im_end|>
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' }}{% else %}{{ '<|im_start|>system
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' + message['content'] + '<|im_end|>
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' }}{% endif %}{% 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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"GptOssForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"eos_token_id": 200002,
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"experts_per_token": 4,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2880,
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"initial_context_length": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 2880,
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"layer_types": [
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"model_type": "gpt_oss",
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"num_attention_heads": 64,
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"num_experts_per_tok": 4,
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"num_hidden_layers": 24,
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"num_key_value_heads": 8,
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"num_local_experts": 32,
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"output_router_logits": false,
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"pad_token_id": 200017,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"beta_fast": 32.0,
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"beta_slow": 1.0,
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"factor": 32.0,
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"original_max_position_embeddings": 4096,
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"rope_type": "yarn",
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"truncate": false
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},
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"rope_theta": 150000,
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"router_aux_loss_coef": 0.9,
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"sliding_window": 128,
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"swiglu_limit": 7.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.3",
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"unsloth_fixed": true,
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"use_cache": true,
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"vocab_size": 201088
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}
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"model.norm.weight": "model-00012-of-00013.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|startoftext|>",
|
||||
"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": "<|reserved_200017|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1af7aff4e39ad377c689737ee7be1e8e77333d28c5ed52a0a137dc68e27557fb
|
||||
size 27868174
|
||||
180
tokenizer_config.json
Normal file
180
tokenizer_config.json
Normal file
@@ -0,0 +1,180 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"199998": {
|
||||
"content": "<|startoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"199999": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200000": {
|
||||
"content": "<|reserved_200000|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200001": {
|
||||
"content": "<|reserved_200001|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200002": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200003": {
|
||||
"content": "<|constrain|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200004": {
|
||||
"content": "<|reserved_200004|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200005": {
|
||||
"content": "<|channel|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200006": {
|
||||
"content": "<|start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200007": {
|
||||
"content": "<|end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200008": {
|
||||
"content": "<|message|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200009": {
|
||||
"content": "<|reserved_200009|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200010": {
|
||||
"content": "<|reserved_200010|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200011": {
|
||||
"content": "<|reserved_200011|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200012": {
|
||||
"content": "<|call|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200013": {
|
||||
"content": "<|reserved_200013|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200014": {
|
||||
"content": "<|reserved_200014|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200015": {
|
||||
"content": "<|reserved_200015|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200016": {
|
||||
"content": "<|reserved_200016|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200017": {
|
||||
"content": "<|reserved_200017|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200018": {
|
||||
"content": "<|endofprompt|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|startoftext|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|reserved_200017|>",
|
||||
"tokenizer_class": "PreTrainedTokenizerFast",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user