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Model: Elinnos/elinnos-sv-v7-ahb-GGUF
Source: Original Platform

README.md

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+---
+library_name: gguf
+base_model: Elinnos/elinnos-sv-v7-ahb-merged
+tags:
+  - qwen2
+  - gguf
+  - quantized
+  - hardware-design
+  - systemverilog
+  - amba-ahb
+  - vlsi
+  - rtl
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# ELINNOS SV-v7-AHB — GGUF Quantized
+
+GGUF quantized versions of [`Elinnos/elinnos-sv-v7-ahb-merged`](https://huggingface.co/Elinnos/elinnos-sv-v7-ahb-merged), the merged ELINNOS SV-v7-AHB model fine-tuned on AMBA AHB hardware design tasks.
+
+## Available Quantizations
+
+| File | Size | Quant | Description |
+|---|---|---|---|
+| `elinnos-sv-v7-ahb-Q4_K_M.gguf` | ~4.4 GB | Q4_K_M | **Recommended** — best quality/size trade-off |
+| `elinnos-sv-v7-ahb-Q8_0.gguf` | ~7.6 GB | Q8_0 | Near-lossless, use if VRAM allows |
+| `elinnos-sv-v7-ahb-f16.gguf` | ~15 GB | F16 | Full precision reference |
+
+## Quick Start (Ollama)
+
+```bash
+ollama run pkelinnos/elinnos-sv-v7-ahb
+```
+
+## Quick Start (llama.cpp)
+
+```bash
+./llama-cli -m elinnos-sv-v7-ahb-Q4_K_M.gguf \
+  --ctx-size 8192 \
+  --temp 0 \
+  -p "Design an AHB-Lite slave with 4 read/write registers."
+```
+
+## Quick Start (Python — llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="elinnos-sv-v7-ahb-Q4_K_M.gguf",
+    n_ctx=8192,
+    n_gpu_layers=-1,
+)
+output = llm.create_chat_completion(
+    messages=[
+        {"role": "system", "content": "You are Elinnos, a hardware design assistant..."},
+        {"role": "user",   "content": "Design an AHB bus matrix for 2 masters and 3 slaves."},
+    ],
+    temperature=0,
+    max_tokens=2048,
+)
+print(output["choices"][0]["message"]["content"])
+```
+
+## Model Details
+
+See the full model card at [`Elinnos/elinnos-sv-v7-ahb`](https://huggingface.co/Elinnos/elinnos-sv-v7-ahb).
+
+- **Base**: Qwen2.5-7B-Instruct
+- **Adapter chain**: v3 → v4 → v5 → v6 → v7 (merged)
+- **LoRA**: r=96, α=192, target: q/k/v/o/gate/up/down proj
+- **Training**: 4 epochs, best eval_loss=0.5408
+- **Conversion**: llama.cpp `convert_hf_to_gguf.py` + `llama-quantize`