初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Flerovium-Llama-3B
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- math
+- code
+- text-generation-inference
+- llama3.2
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+pipeline_tag: text-generation
+---
+![6.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ro8JJK0IQgseIvg2SgMb3.png)
+
+# **Flerovium-Llama-3B**
+
+> **Flerovium-Llama-3B** is a compact, general-purpose language model based on the powerful **llama 3.2** (llama) architecture. It is fine-tuned for a broad range of tasks including **mathematical reasoning**, **code generation**, and **natural language understanding**, making it a versatile choice for developers, students, and researchers seeking reliable performance in a lightweight model.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Flerovium-Llama-3B-GGUF](https://huggingface.co/prithivMLmods/Flerovium-Llama-3B-GGUF)
+
+---
+
+## **Key Features**
+
+1. **LLaMA 3.2 Backbone**
+   Built on **Meta’s LLaMA 3.2 (3B)** architecture, offering state-of-the-art performance in a compact footprint with better instruction-following and multilingual support.
+
+2. **Multi-Task Fine-Tuning**
+   Finetuned on a modular and diverse dataset combining math, code, and general-purpose tasks—enabling clear explanations, problem solving, and practical utility.
+
+3. **Strong Mathematical Reasoning**
+   Handles algebra, calculus, logic, and numerical problems with step-by-step clarity. Ideal for tutoring and academic use cases.
+
+4. **Coding Capabilities**
+   Understands and generates clean, bug-free code in Python, JavaScript, C++, and more. Also excels at debugging, documentation, and logic explanations.
+
+5. **General-Purpose Utility**
+   Performs well across everyday reasoning tasks—summarization, Q\&A, content drafting, and structured generation (Markdown, LaTeX, JSON).
+
+6. **Efficient & Deployable**
+   With only 3 billion parameters, Flerovium-Llama-3B is resource-efficient and suitable for local deployment, offline tools, and edge AI setups.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Flerovium-Llama-3B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain how to solve a quadratic equation step-by-step."
+
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant for math and coding."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## **Intended Use**
+
+* General-purpose text and reasoning
+* Math tutoring and problem-solving
+* Code generation, review, and debugging
+* Content drafting in Markdown, LaTeX, and JSON
+* Lightweight deployment in educational and developer environments
+
+---
+
+## **Limitations**
+
+* Limited context length compared to large models (>7B)
+* May require prompt refinement for very complex code/math problems
+* Not ideal for long-form creative writing or deep conversational tasks
+* Knowledge is limited to training data (no real-time web search)
+
+---
+
+## **References**
+
+1. [LLaMA 3 Technical Report (Meta)](https://ai.meta.com/llama/)
+2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)