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Model: JustScriptzz/nexus-plus-v2
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+- it
+tags:
+- qwen3
+- qlora
+- fine-tuned
+- instruction-tuning
+- peft
+- safetensors
+library_name: peft
+base_model: Qwen/Qwen3-4B-Base
+---
+
+# Nexus Plus v2
+
+A **4.05B parameter** causal language model fine-tuned from [Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base) using QLoRA on ~50k instruction examples.
+
+## Try it Online
+
+Test the model directly in your browser:
+
+[![Try Chat](https://img.shields.io/badge/Try%20Live-Streamlit-FF4B4B?logo=streamlit&logoColor=white)](https://try-nexus-ai.streamlit.app)
+[![GitHub](https://img.shields.io/badge/View%20Source-181717?logo=github)](https://github.com/JustScriptzz/nexus-smAll-web)
+
+## Model Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | Qwen/Qwen3-4B-Base |
+| Total parameters | 4.05B |
+| Trainable parameters | 33M (LoRA) |
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| Target modules | q_proj, v_proj |
+| Quantization | 4-bit (QLoRA) |
+| Precision | BF16 |
+
+## How to Use
+
+### With PEFT (adapters only)
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3-4B-Base",
+    torch_dtype="auto",
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(base_model, "JustScriptzz/nexus-plus-v2")
+tokenizer = AutoTokenizer.from_pretrained("JustScriptzz/nexus-plus-v2")
+
+messages = [
+    {"role": "user", "content": "What is Python?"}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Merged model (recommended)
+
+This repo contains the fully merged model (LoRA weights baked into base). No PEFT needed:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "JustScriptzz/nexus-plus-v2",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("JustScriptzz/nexus-plus-v2")
+
+messages = [
+    {"role": "user", "content": "What is Python?"}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training
+
+- **Base model**: Qwen3-4B-Base (4.05B params, 4-bit quantized)
+- **Dataset**: ~50k instruction examples (Dolly-15k, synthetic QA, general instruction data)
+- **Method**: QLoRA (rank 16, alpha 32, targeting q_proj and v_proj)
+- **Hardware**: RTX 5060 Ti 16GB
+- **Training time**: ~7 hours
+- **Steps**: 5,634
+- **Final loss**: 1.45
+
+## Limitations
+
+- Fine-tuned on a relatively small dataset
+- May not generalize well to all domains
+- Best used as a learning experiment or starting point for further fine-tuning
+
+## License
+
+Apache 2.0