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Model: jalpan04/qwen-researcher
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- qwen
+- qwen2
+- gguf
+- f16
+- computer-science
+- research
+- instruction-tuning
+model_creator: jalpan04
+model_name: Qwen Research Assistant
+pipeline_tag: text-generation
+quantized_by: f16
+---
+
+# Qwen Researcher (0.5B - GGUF)
+
+A specialized version of **Qwen2.5-0.5B-Instruct** fine-tuned for Computer Science research assistance. This model has been instruction-tuned on a curated subset of 2,000 arXiv Computer Science papers to provide academic summaries and technical insights.
+
+## Model Details
+
+- **Developed by:** Jalpan04
+- **Model type:** Causal Language Model
+- **Language(s):** English
+- **License:** Apache-2.0
+- **Fine-tuned from model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+- **Training Method:** QLoRA (Rank 16, Alpha 32)
+- **Dataset:** arXiv Computer Science Metadata (Instruction-formatted)
+
+## Usage
+
+### Local Deployment (Ollama)
+
+1. Create a `Modelfile`:
+```dockerfile
+FROM ./qwen-resercher.gguf
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .User }}<|im_start|>user
+{{ .User }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ .Output }}<|im_end|>"""
+PARAMETER stop "<|im_start|>"
+PARAMETER stop "<|im_end|>"
+SYSTEM "You are a professional computer science researcher. Provide academic, detailed information based on research abstracts."
+```
+
+2. Run in terminal:
+```bash
+ollama create qwen-researcher -f Modelfile
+ollama run qwen-researcher
+```
+
+### Python (llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+llm = Llama.from_pretrained(
+    repo_id="jalpan04/qwen-researcher",
+    filename="qwen-resercher.gguf",
+)
+
+response = llm.create_chat_completion(
+    messages = [
+        {"role": "system", "content": "You are a CS researcher."},
+        {"role": "user", "content": "Summarize the latest trends in Neural Program Synthesis."}
+    ]
+)
+print(response["choices"][0]["message"]["content"])
+```
+
+## Training Procedure
+
+The model was trained on an **NVIDIA RTX 4060 (8GB)** using the following setup:
+- **Optimization:** 4-bit NormalFloat (nf4) quantization.
+- **Precision:** BFloat16 for stability on 40-series hardware.
+- **Learning Rate:** 2e-4.
+- **Batch Size:** 1 with Gradient Accumulation (16 steps).
+- **Sequence Length:** 1024 tokens.
+
+## Limitations
+
+As a 0.5B parameter model, this is highly efficient but may exhibit hallucinations compared to larger models (7B+). It is best used for summarization and quick technical lookups rather than complex logical reasoning.