初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Qwen3-4B-ft-bf16
Source: Original Platform

README.md

@@ -0,0 +1,107 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- moe
+- moderately abliterated variant
+- text-generation-inference
+---
+
+![FMjPew6Vjrp4FvKe1Uz_T.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/cMq6BmD-1cEiOngYBL3Wh.png)
+
+# **Qwen3-4B-ft-bf16**
+
+> **Qwen3-4B-ft-bf16** is a fine-tuned, moderately abliterated version of the Qwen3-4B model. Designed for **enhanced context awareness** and **controlled expressiveness**, this model balances precision with creativity across a wide range of tasks—from complex reasoning to natural dialogue, code generation, and multilingual understanding.
+
+### Key Features:
+
+- **Improved Context Awareness**  
+  Retains and utilizes long-range contextual information effectively, making it ideal for long-form conversations, document understanding, and summarization tasks.
+
+- **Moderate Abliteration**  
+  Introduces measured behavioral flexibility that enhances creativity and adaptability while maintaining reliability, alignment, and safety in outputs.
+
+- **Dual Thinking Modes**  
+  Supports dynamic switching between *thinking* mode (for math, logic, and coding) and *non-thinking* mode (for general-purpose conversations), ensuring optimal task matching.
+
+- **Multilingual Mastery**  
+  Excels in over 100 languages and dialects for translation, multilingual chat, and cross-lingual reasoning.
+
+- **Tool-Ready Agent Capabilities**  
+  Designed to integrate with tool APIs and complex workflows, with consistent performance in both thinking and non-thinking contexts.
+
+---
+
+## Quickstart with Hugging Face Transformers🤗
+
+```bash
+pip install transformers==4.51.3
+pip install huggingface_hub[hf_xet]
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Qwen3-4B-ft-bf16"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Define input
+prompt = "Describe how renewable energy impacts economic development."
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Generate output
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# Parse thinking content
+try:
+    index = len(output_ids) - output_ids[::-1].index(151668)
+except ValueError:
+    index = 0
+
+thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip()
+content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()
+
+print("thinking content:", thinking_content)
+print("content:", content)
+```
+
+---
+
+## Best Practices
+
+- **Sampling Settings**:
+  - *Thinking mode*: `temperature=0.6`, `top_p=0.95`, `top_k=20`
+  - *Non-thinking mode*: `temperature=0.7`, `top_p=0.8`, `top_k=20`
+
+- **Token Length**:
+  - Standard: `32768 tokens`
+  - Extended Reasoning Tasks: `up to 38912 tokens`
+
+- **Prompt Design**:
+  - **Math Problems**: Add `"Please reason step by step, and put your final answer within \boxed{}."`
+  - **MCQs**: Format answers as `{"answer": "B"}` for easy parsing.
+  - **Multi-turn**: Omit thinking logs in conversation history for cleaner context.