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Model: bfavro73/qwen2.5-coder-7b-pandas-dpo-aligned
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# qwen2.5-coder-7b-pandas-dpo-aligned
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+## Introduction
+
+Fine-tuned version of Qwen's Code-Specific large language model, Qwen2.5-coder-7b. Qwen-2.5 Coder has six mainstream model sizes: 0.5, 1.5, 3, 7, 14 and 32 billion 
+parameter models to meet developer needs. The 7B model version provides a great balance between model representationl capacity and runtime requirements. This 7 Billion parameter
+model was fine-tuned using offline DPO and a preference dataset for Python data analysis.
+
+Available in GGUF format.
+- Q4_K_M recommended for systems with at least 8GB of RAM. Model file size: 4.68GB
+
+**Context Window:** up to 128K tokens
+  
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+
+- **License:** Apache 2.0
+- **Finetuned from model:** [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
+
+
+
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+Provides a good foundation for real-world applications such as Coding Agents. It has enhanced coding capabilities and
+maintains strengths in mathematics and general competencies.
+
+
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+
+
+
+### Compute Infrastructure
+
+The memory you need depends heavily on precision/quantization and context length. *Always consider headroom for runtime and cache!*
+KV/Key-Value cache (scales with context length and batch size).
+
+Considering standard Memory (bytes)≈params×bytes per parameter rule of thumb:
+
+**Example 1 (CPU):** Running on CPU (llama.cpp / GGUF):
+
+A 7B model at 4‑bit needs about 3.5 – 4GB (7×10^9x0.5) for weights. With runtime overhead, KV cache, and your app, a practical minimum is:
+
+- 16 GB RAM (barely)
+- 32 GB RAM recommended for smoother operation
+
+**Example 2 (GPU):** For local GPU inference with decent context and speed:
+
+- Aim for 10–12 GB VRAM (7×10^9×0.5) for a 4‑bit Qwen2.5‑Coder‑7B.
+- Aim for 16 GB VRAM (7×10^9×2) if you want FP16 or very long contexts.
+
+
+#### Hardware
+
+No GPU offload:
+
+**Absolute Minimum:** 4 vCPUs (4 cores / threads) to run 7B 4‑bit model, but generation will likely feel sluggish, especially with longer responses.
+
+**Realistic Minimum:** For heavier coding uses with multiple users and long context: 8–12 vCPUs, allows:
+
+- 8–10 threads for the model.
+- Remaining cores for the app and OS.
+
+With GPU offload performing all the matrix multiplication 4 vCPUs are sufficient.
+
+
+## More Information [optional]
+
+[More Information Needed]
+