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Model: prithivMLmods/TOI-157-Phi-4-Reasoning-Mini Source: Original Platform
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README.md
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README.md
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---
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license: mit
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base_model:
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- microsoft/Phi-4-mini-reasoning
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- trl
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- text-generation-inference
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- math
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- code
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---
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# **TOI-157-Phi-4-Reasoning-Mini**
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> **TOI-157-Phi-4-Reasoning-Mini** is a reasoning-focused model fine-tuned on **Microsoft’s Phi-4-mini-reasoning** for **Edge-level Abliterated Reasoning** and optimized **polished token probabilities**, enhancing balanced **multilingual generation** across mathematics and general-purpose reasoning.
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> It specializes in **event-driven logic**, **structured analysis**, and precise probabilistic modeling—making it an ideal tool for researchers, educators, and developers working with uncertainty and structured reasoning.
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## **Key Features**
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1. **Abliterated Reasoning**
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Enhanced reasoning precision through polished token probability distributions in Phi-based models, ensuring balanced and context-aware outputs.
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2. **Event Simulation & Logical Analysis**
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Models random events, probability-driven reasoning, and logical decision-making with strong consistency.
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3. **Multilingual Mathematical & General-Purpose Problem Solving**
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Delivers robust performance in **math**, **probability**, and **structured multilingual tasks**, enabling wide applicability in global research and education.
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4. **Hybrid Symbolic-Probabilistic Thinking**
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Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.
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5. **Structured Output Mastery**
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Generates well-structured outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, supporting technical workflows and data-driven research.
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6. **Optimized Lightweight Footprint**
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Compact **mini parameter size**, deployable on **edge devices**, **offline clusters**, and **mid-range GPUs**, while maintaining reasoning quality.
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## **Quickstart with Transformers**
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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torch.random.manual_seed(0)
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model_id = "prithivMLmods/TOI-157-Phi-4-Reasoning-Mini"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="cuda",
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torch_dtype="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [{
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"role": "user",
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"content": "How to solve 3*x^2 + 4*x + 5 = 1?"
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}]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_dict=True,
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return_tensors="pt",
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)
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outputs = model.generate(
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**inputs.to(model.device),
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max_new_tokens=32768,
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temperature=0.8,
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top_p=0.95,
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do_sample=True,
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)
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outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])
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print(outputs[0])
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```
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## **Intended Use**
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* Balanced multilingual reasoning and probability modeling
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* Event simulation, uncertainty analysis, and structured problem solving
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* Educational and research-focused reasoning tasks
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* Lightweight deployment in constrained environments
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* Technical content and structured data generation
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## **Limitations**
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* Focused on reasoning and mathematics—less suited for creative writing
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* Smaller size may limit depth on highly complex, multi-step tasks
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* Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone.
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}
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{{ '<|system|>Your name is Phi, an AI math expert developed by Microsoft.' }}{% for message in messages %}{% if message['role'] == 'system' %} {{ message['content'] }}{% if 'tools' in message and message['tools'] is not none %}{{ '<|tool|>' + message['tools'] + '<|/tool|>' }}{% endif %}{% endif %}{% endfor %}{{ '<|end|>' }}{% for message in messages %}{% if message['role'] != 'system' %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}
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config.json
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"Phi3ForCausalLM"
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|
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"special": false
|
||||
},
|
||||
"200025": {
|
||||
"content": "<|tool_call|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200026": {
|
||||
"content": "<|/tool_call|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200027": {
|
||||
"content": "<|tool_response|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200028": {
|
||||
"content": "<|tag|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"extra_special_tokens": {},
|
||||
"max_length": 1024,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"stride": 0,
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"truncation_side": "right",
|
||||
"truncation_strategy": "longest_first",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
3
vocab.json
Normal file
3
vocab.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6cb65a857824fa6615bb1782d95d882617a8bbce1da0317118586b36f39e98bd
|
||||
size 3910310
|
||||
Reference in New Issue
Block a user