初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Gliese-4B-OSS-0410
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- prithivMLmods/Kepler-Qwen3-4B-Super-Thinking
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- trl
+- pytorch
+- text-generation-inference
+- thinking
+- gpt_oss
+- math
+- code
+- smoothing
+- agent
+---
+
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/xwNz8R9cHHBArUKbTKs6U.png)
+
+# **Gliese-4B-OSS-0410**
+
+> **Gliese-4B-OSS-0410** is a reasoning-focused model fine-tuned on **Qwen-4B** for enhanced **reasoning** and **polished token probability distributions**, delivering balanced **multilingual generation** across mathematics and general-purpose reasoning tasks.
+> The model is fine-tuned on curated **GPT-OSS synthetic dataset entries**, improving its ability to handle structured reasoning, probabilistic inference, and multilingual tasks with precision.
+
+> [!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Gliese-4B-OSS-0410-GGUF](https://huggingface.co/prithivMLmods/Gliese-4B-OSS-0410-GGUF)
+
+---
+
+## Key Features
+
+1. **Enhanced Reasoning Precision**
+   Refined token probability distributions improve reasoning quality and ensure balanced, context-aware outputs.
+
+2. **Event Simulation and Logical Analysis**
+   Capable of modeling random events, probability-driven reasoning, and structured decision-making with strong logical consistency.
+
+3. **Multilingual Mathematical and General-Purpose Problem Solving**
+   Delivers robust performance in **mathematics**, **probability**, and **structured multilingual tasks**, enabling broad applicability in research and education.
+
+4. **Hybrid Symbolic–Probabilistic Thinking**
+   Combines structured logic, probabilistic inference, and reasoning fluency to improve performance on uncertainty-driven tasks.
+
+5. **Structured Output Generation**
+   Generates well-formatted outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, supporting technical workflows and data-oriented research.
+
+6. **Optimized Lightweight Footprint**
+   With **4B parameters**, it runs efficiently on **mid-range GPUs**, **offline clusters**, and **edge devices** without compromising reasoning performance.
+
+---
+
+## Quickstart with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Gliese-4B-OSS-0410"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."
+
+messages = [
+    {"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and multilingual problem-solving."},
+    {"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
+
+* Balanced multilingual reasoning and probability modeling
+* Event simulation, uncertainty analysis, and structured problem solving
+* Educational and research-focused reasoning tasks
+* Deployment in mid-resource environments with efficient inference
+* Structured technical content and data format generation
+
+---
+
+## Limitations
+
+* Primarily focused on reasoning and mathematics; less suited for creative writing
+* Despite its 4B size, extremely complex multi-hop reasoning tasks may remain challenging
+* Prioritizes structured reasoning and probabilistic accuracy over conversational tone
+* May produce inconsistent results with **very long contexts** or **cross-domain multi-document inputs**