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Model: FinaPolat/phi4_adaptableIE_v2 Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: microsoft/phi-4
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- phi-4
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- information-extraction
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- ner
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- relation-extraction
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- knowledge-graph
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- slm
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model_creator: FinaPolat
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language:
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- en
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---
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# Phi-4-AdaptableIE: Efficient Adaptive Knowledge Graph Extraction
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#### This model has gguf version: https://huggingface.co/FinaPolat/phi4_adaptableIE_v2-gguf
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Phi-4-AdaptableIE is a specialized **14.7B parameter Small Language Model (SLM)** optimized via **Supervised Fine-Tuning (SFT)** for high-precision, **Joint Named Entity Recognition (NER) and Relation Extraction (RE)**.
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Unlike traditional multi-stage pipelines that are prone to cascading error propagation, this model performs entity identification and relational mapping in a single cohesive pass. It is designed to be **ontology-adaptive**, allowing it to conform to dynamic, unseen schemas at inference time through a specialized **Structured Prompt Architecture**.
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## 🚀 Model Highlights
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- **Joint Extraction:** Unified NER + RE reducing pipeline complexity.
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- **Ontology-Adaptive:** Zero-shot adaptation to diverse domains (Astronomy, Music, Healthcare, etc.) via dynamic schema variables.
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- **Local & Private:** Optimized for **local CPU-only inference** (via GGUF/Ollama - FinaPolat/phi4_adaptableIE_v2-gguf ), ensuring data sovereignty without external API dependencies.
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- **Instruction Aligned:** Fine-tuned to follow strict negative constraints, ensuring zero conversational filler in outputs.
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## 🛠 Methodology
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The model was fine-tuned using **QLoRA** on the **WebNLG** subset of the **Text2KGBench** benchmark. The training process focused on **Conversational Alignment**, ensuring the model treats extraction as a strict logical mapping:
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`Prompt = f(task, schema, example, text)`
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---
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## 📝 Prompting Strategy
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To achieve high-fidelity extraction, the model requires a specific prompt structure.
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### 1. System Prompt
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```json
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{
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"role": "system",
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"content": "You are a helpful AI assistant specializing in Information Extraction tasks such as Named Entity Recognition and Relation Extraction. Follow the instructions given by the user."
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}
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```
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### 2. User Prompt Template
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```css
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Information Extraction is the process of automatically identifying and extracting structured information from unstructured text data... [Context] ...
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Always extract numbers, dates, and currency values regardless of the specific task.
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The task at hand is {task}.
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Here is an example of task execution:
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{example}
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Analyze the text and targets carefully, identify relevant information.
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Extract the information in the following format: `{output_format}`.
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If no matching entities are found, return an empty list: [].
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Please provide only the extracted information without any explanations.
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Schema: {schema}
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Text: {inputs}
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```
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### 3. 💻 Usage Examples
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Option 1: Transformers (Single GPU)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "FinaPolat/phi4_adaptableIE_v2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
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task = "Joint NER and RE"
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schema = "['CelestialBody', 'apoapsis', 'averageSpeed']"
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inputs = "(19255) 1994 VK8 has an average speed of 4.56 km per second."
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output_format = "[('subject', 'predicate', 'object')]"
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prompt = f"Task: {task}\nSchema: {schema}\nText: {inputs}\nExtract:"
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input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**input_ids, max_new_tokens=256, temperature=0.0)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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Option 2: High-Throughput Batch Inference (vLLM)
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(
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model="FinaPolat/phi4_adaptableIE_v2",
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dtype="bfloat16",
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trust_remote_code=True,
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gpu_memory_utilization=0.9,
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max_model_len=3000,
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enforce_eager=True,
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distributed_executor_backend="uni"
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)
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sampling_params = SamplingParams(temperature=0.0, max_tokens=256)
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outputs = llm.chat(batch_prompts, sampling_params=sampling_params, use_tqdm=True)
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```
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### 4. 📦 Deployment & Hardware Requirements
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| Deployment Mode | Quantization | Hardware Requirement | Target Latency |
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|-----------------|--------------|------------------------------------------|----------------|
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| Server-side | BF16 | 1× NVIDIA A100 / RTX 4090 (24GB+) | Ultra-Low |
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| Local Consumer | 4-bit GGUF | 16GB RAM (Apple Silicon / PC CPU) | Moderate |
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For CPU-only local execution, refer to the GGUF version: phi4_adaptableIE_v2-gguf📜
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### 5. Citation & Credits
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If you use this model in your research, please cite the Text2KGBench framework and the Microsoft Phi-4 technical report and our work:
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https://github.com/FinaPolat/ENEXA_adaptable_extraction
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Video: https://www.youtube.com/watch?v=your-video-
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{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 100257,
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"torch_dtype": "bfloat16",
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"eos_token_id": 100265,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 17920,
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"max_position_embeddings": 16384,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 10,
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"original_max_position_embeddings": 16384,
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"pad_token_id": 100351,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_theta": 250000,
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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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"unsloth_version": "2026.1.3",
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"use_cache": true,
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"vocab_size": 100352
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}
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}
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||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
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||||
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||||
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||||
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||||
},
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||||
"eos_token": {
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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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||||
"pad_token": {
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||||
"content": "<|dummy_87|>",
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||||
"normalized": false,
|
||||
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|
||||
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|
||||
},
|
||||
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||||
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||||
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||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
501273
tokenizer.json
Normal file
501273
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
791
tokenizer_config.json
Normal file
791
tokenizer_config.json
Normal file
@@ -0,0 +1,791 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100256": {
|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100257": {
|
||||
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|
||||
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|
||||
"normalized": false,
|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100258": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100259": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100260": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100261": {
|
||||
"content": "<|dummy_1|>",
|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100262": {
|
||||
"content": "<|dummy_2|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100263": {
|
||||
"content": "<|dummy_3|>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100264": {
|
||||
"content": "<|im_start|>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100265": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100266": {
|
||||
"content": "<|im_sep|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100267": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
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|
||||
},
|
||||
"100268": {
|
||||
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|
||||
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|
||||
"normalized": false,
|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100269": {
|
||||
"content": "<|dummy_6|>",
|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100270": {
|
||||
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|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100271": {
|
||||
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|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100272": {
|
||||
"content": "<|dummy_9|>",
|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100273": {
|
||||
"content": "<|dummy_10|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100274": {
|
||||
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|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100275": {
|
||||
"content": "<|dummy_12|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100276": {
|
||||
"content": "<|endofprompt|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100277": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100278": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100279": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100280": {
|
||||
"content": "<|dummy_16|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100281": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100282": {
|
||||
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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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|
||||
"100283": {
|
||||
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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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|
||||
"unk_token": "�",
|
||||
"chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}"
|
||||
}
|
||||
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