初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/SmolLM2-Rethink-135M
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- sequelbox/Celestia3-DeepSeek-R1-0528
+base_model:
+- HuggingFaceTB/SmolLM2-135M-Instruct
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- trl
+- text-generation-inference
+- re-think
+- reasoning
+---
+ 
+![sm2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jhbqEfst42Fa2oDNZwTyV.png)
+
+# **SmolLM2-Rethink-135M**
+
+> **SmolLM2-Rethink-135M** is an experimental lightweight model trained on the **Celestia3-DeepSeek-R1-0528** reasoning dataset. Based on the **SmolLM2-135M-Instruct** architecture, this model is specifically optimized for reasoning, structured outputs, and efficient small-scale deployment. Despite its compact size (135M parameters), it demonstrates strong capabilities in logical deduction, conversational coherence, and lightweight inference tasks.
+
+---
+
+## **Key Highlights**
+
+1. **Compact & Efficient**
+   Lightweight architecture (135M) suitable for fast inference, mobile applications, and edge deployment.
+
+2. **Reasoning-Centric Training**
+   Fine-tuned on high-quality reasoning and instruction datasets like **Celestia3-DeepSeek-R1-0528**, focusing on multi-step logical thinking.
+
+3. **Low-Resource Optimization**
+   Designed to run effectively on CPUs or single-GPU setups with minimal memory footprint.
+
+4. **Structured Outputs**
+   Supports generation of clean, structured content including lists, steps, tables, and JSON-like responses.
+
+---
+
+## **Quickstart with 🤗 Transformers**
+
+```python
+%%capture
+!pip install transformers
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "prithivMLmods/SmolLM2-Rethink-135M"
+device = "cuda"  # or "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+
+messages = [{"role": "user", "content": "What is gravity?"}]
+input_text = tokenizer.apply_chat_template(messages, tokenize=False)
+print(input_text)
+
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    temperature=0.2,
+    top_p=0.9,
+    do_sample=True
+)
+
+print(tokenizer.decode(outputs[0]))
+```
+
+---
+
+## **Intended Use**
+
+* **Instruction Following & QA**
+  Good for answering simple questions, following short instructions, and general user interactions.
+
+* **Educational Tools**
+  Suitable for lightweight tutoring bots or classroom assistants on low-compute setups.
+
+* **Reasoning Tasks**
+  Performs well on logic puzzles, multi-step reasoning, and chain-of-thought queries.
+
+* **Prototype Agents & Microservices**
+  Can be deployed in memory-efficient environments or as modular AI components.
+
+---
+
+## **Limitations**
+
+1. **Limited Knowledge Capacity**
+   Due to small parameter size, lacks the depth and breadth of large-scale models.
+
+2. **Short-Term Context Handling**
+   Performs best with short to moderate-length prompts; lacks extended context support.
+
+3. **Creative Generation Limitations**
+   Output may lack diversity or depth in open-ended storytelling or imaginative tasks.
+
+4. **Token Budget**
+   Smaller output range; optimized for shorter and structured completions.
+
+5. **Basic Multilingual Support**
+   Some support for multilingual input, but less accurate than larger multilingual models.