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Model: arshaan-nazir/qwen2.5-3b-humanizer-merged Source: Original Platform
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B-Instruct
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tags:
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- text-generation
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- humanizer
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- rewriter
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- qwen2.5
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- qlora
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- merged
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- conversational
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pipeline_tag: text-generation
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---
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# Qwen2.5-3B AI Text Humanizer (Merged)
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A fine-tuned version of **Qwen/Qwen2.5-3B-Instruct** that rewrites AI-generated text to sound natural and human-written. This is the **fully merged** model — the LoRA adapter weights have been baked directly into the base model, making it compatible with fast inference engines like **vLLM**.
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> ⚠️ This is a **private model**. You need a HuggingFace token with read access to load it.
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## Model Details
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| Property | Value |
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|---|---|
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| Base model | `Qwen/Qwen2.5-3B-Instruct` |
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| Fine-tuning method | QLoRA (r=64, α=128) |
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| Training dataset | `qwertyuiopasdfg/English_humanize` (20k rows) |
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| Adapter repo | `arshaan-nazir/qwen2.5-3b-humanizer-qlora` |
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| Model type | Causal LM — merged weights |
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| Language | English |
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| License | Apache 2.0 |
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## What It Does
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Takes AI-generated text as input and rewrites it to:
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- Sound natural and conversational
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- Use contractions where appropriate
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- Vary sentence length and structure
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- Avoid stiff, formal phrasing
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- Preserve all original facts
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## How to Use
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### With Transformers
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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HF_TOKEN = "hf_YOUR_TOKEN_HERE" # needs read access to this repo
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print("Loading tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(
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"arshaan-nazir/qwen2.5-3b-humanizer-merged",
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token=HF_TOKEN,
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)
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print("Loading model...")
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model = AutoModelForCausalLM.from_pretrained(
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"arshaan-nazir/qwen2.5-3b-humanizer-merged",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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token=HF_TOKEN,
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)
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model.eval()
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SYSTEM = """You are a helpful editor.
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Rewrite the input so it sounds natural, clear, and conversational while preserving every fact.
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Rules:
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- Vary sentence length and structure (mix short and long).
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- Use contractions where natural.
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- Avoid stiff/overly formal phrases.
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- No bullet points, headings, or numbered lists.
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- Keep paragraphs reasonable (no more than 5 sentences per paragraph).
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- Output ONLY the rewritten text (no preamble, no labels).
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"""
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def humanize(text):
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msgs = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": f"Rewrite the following so it sounds fully human-written.\n\nTEXT:\n{text}\n\nREWRITE:"}
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]
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prompt = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
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with torch.no_grad():
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output = model.generate(
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input_ids,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.75,
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top_p=0.92,
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repetition_penalty=1.08,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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return tokenizer.decode(output[0, input_ids.shape[1]:], skip_special_tokens=True)
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text = "The utilization of artificial intelligence has resulted in significant advancements."
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print(humanize(text))
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```
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### With vLLM (recommended — fastest)
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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HF_TOKEN = "hf_YOUR_TOKEN_HERE"
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tokenizer = AutoTokenizer.from_pretrained(
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"arshaan-nazir/qwen2.5-3b-humanizer-merged",
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token=HF_TOKEN,
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)
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llm = LLM(
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model="arshaan-nazir/qwen2.5-3b-humanizer-merged",
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dtype="bfloat16",
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gpu_memory_utilization=0.90,
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max_model_len=4096,
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enforce_eager=True,
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)
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sampling_params = SamplingParams(
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temperature=0.75,
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top_p=0.92,
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repetition_penalty=1.08,
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max_tokens=1024,
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)
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SYSTEM = """You are a helpful editor.
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Rewrite the input so it sounds natural, clear, and conversational while preserving every fact.
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Rules:
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- Vary sentence length and structure (mix short and long).
|
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- Use contractions where natural.
|
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- Avoid stiff/overly formal phrases.
|
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- No bullet points, headings, or numbered lists.
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- Keep paragraphs reasonable (no more than 5 sentences per paragraph).
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- Output ONLY the rewritten text (no preamble, no labels).
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"""
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text = "Your AI-generated text here..."
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msgs = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": f"Rewrite the following so it sounds fully human-written.\n\nTEXT:\n{text}\n\nREWRITE:"}
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]
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prompt = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
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outputs = llm.generate([prompt], sampling_params)
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print(outputs[0].outputs[0].text.strip())
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```
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## Recommended Inference Settings
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| Parameter | Value |
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|---|---|
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| `temperature` | 0.75 |
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| `top_p` | 0.92 |
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| `repetition_penalty` | 1.08 |
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| `max_new_tokens` | 512–1024 |
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## Training Details
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- **Base model:** `Qwen/Qwen2.5-3B-Instruct`
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- **Method:** QLoRA with 4-bit NF4 quantization during training
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- **LoRA rank:** 64, alpha: 128
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- **Target modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
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- **Dataset:** `qwertyuiopasdfg/English_humanize` — 20k (input, output) pairs of AI-generated vs human-written text
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- **Epochs:** 3
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- **Optimizer:** paged_adamw_8bit
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- **Learning rate:** 2e-4 with cosine schedule
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## Difference from Adapter Repo
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| | [`qwen2.5-3b-humanizer-qlora`](https://huggingface.co/arshaan-nazir/qwen2.5-3b-humanizer-qlora) | This repo |
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|---|---|---|
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| Type | LoRA adapter only | Fully merged model |
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| Requires base model | ✅ Yes | ❌ No |
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| vLLM compatible | ❌ No | ✅ Yes |
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| Size | ~120 MB | ~6 GB |
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| Load time | Slower (loads base + adapter) | Faster (single model) |
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## Live Demo
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Try it at: [arshaan-nazir/ai-text-humanizer](https://huggingface.co/spaces/arshaan-nazir/ai-text-humanizer-1771785713)
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## Developer
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**arshaan-nazir** — [HuggingFace Profile](https://huggingface.co/arshaan-nazir)
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config.json
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config.json
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{
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"_name_or_path": "Qwen/Qwen2.5-3B-Instruct",
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"Qwen2ForCausalLM"
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"use_sliding_window": false,
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"top_p": 0.8,
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"transformers_version": "4.44.2"
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}
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
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||||
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||||
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||||
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"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
303283
tokenizer.json
Normal file
303283
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"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
|
||||
}
|
||||
},
|
||||
"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,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\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 {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) 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{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
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