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Model: SolusOps/Lizzy-7B-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: flwrlabs/Lizzy-7B
+tags:
+  - llama-cpp
+  - gguf
+  - olmo2
+  - quantized
+  - uk-english
+  - agentic
+  - function-calling
+  - lizzy-7B
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Lizzy-7B GGUF Quants
+
+> 🚨 **Update:** Flower Labs has officially released their native GGUF quants.
+> I highly recommend transitioning to their repository for the most stable inference and the corrected 32k context window: **[flwrlabs/Lizzy-7B-GGUF](https://huggingface.co/flwrlabs/Lizzy-7B-GGUF)**.
+> 
+> *Note: During testing, I came across a bug with rope/context length issue, which has been patched in the official release. Thanks to the 250+ community members who tested this early build!*
+
+**Quantized by [SolusOps](https://huggingface.co/SolusOps)** 
+
+**Original model:** [FlowerLabs/Lizzy-7B](https://huggingface.co/flwrlabs/Lizzy-7B)
+
+**Official Quants:** [flwrlabs/Lizzy-7B-GGUF](https://huggingface.co/flwrlabs/Lizzy-7B-GGUF)
+
+## About This Repo
+
+This repository provides llama.cpp-compatible GGUF quants of **Lizzy-7B**, a UK-centric 7B language model built by [Flower Labs](https://flower.ai).
+Refer to the [original model card](https://huggingface.co/flwrlabs/Lizzy-7B) for more details on the model.
+
+## Available Quants
+
+| File | Quant | Size | Use Case |
+|---|---|---|---|
+| `Lizzy-7B-f16.gguf` | F16 | ~14.6 GB | needs 20GB+ VRAM or CPU offload. |
+| `Lizzy-7B-Q8_0.gguf` | Q8_0 | ~7.7 GB | **Recommended** fits 12GB VRAM with excellent context headroom. |
+| `Lizzy-7B-Q6_K.gguf` | Q6_K | ~5.9 GB | for 10GB–12GB GPUs looking to maximize context size. |
+| `Lizzy-7B-Q5_K_M.gguf` | Q5_K_M | ~5.1 GB | 8GB VRAM |
+| `Lizzy-7B-Q4_K_M.gguf` | Q4_K_M | ~4.1 GB | 6GB–8GB GPUs. |
+| `Lizzy-7B-Q3_K_M.gguf` | Q3_K_M | ~3.5 GB | edge devices, 4GB GPUs, or older laptops. |
+
+## Hardware Tested
+
+| Hardware | Quant | n_ctx | Speed |
+|---|---|---|---|
+| RTX 3060 12GB | Q8_0 | 8192 | ~23 tok/s |
+| RTX 3060 12GB | F16 | 4096 | Slower (VRAM overflow to RAM) |
+
+## Conversion Notes
+
+### 1. Architecture: OLMo 2 Post-Norm Tensor Mapping
+
+Lizzy-7B uses a Post-Norm variant of OLMo 2.
+The standard convert_hf_to_gguf.py script does not recognise Flower Labs tensor naming conventions (post_attn_norm, post_mlp_norm) 
+and will fail or silently produce a broken file.
+The fix was to register a LizzyForCausalLM model class in the llama.cpp conversion script,
+subclassing Olmo2Model and overriding modify_tensors() to remap the four divergent tensor names:
+
+```
+python@ModelBase.register("LizzyForCausalLM")
+class LizzyModel(Olmo2Model):
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+
+        # 1. Lizzy: post_attn_norm -> llama.cpp: post_attention_norm
+        if name.endswith(".post_attn_norm.weight"):
+            yield (f"blk.{bid}.post_attention_norm.weight", data_torch)
+            return
+
+        # 2. Lizzy: post_mlp_norm -> llama.cpp: post_ffw_norm
+        if name.endswith(".post_mlp_norm.weight"):
+            yield (f"blk.{bid}.post_ffw_norm.weight", data_torch)
+            return
+
+        # 3. QK-Norms these mapped correctly via standard paths
+        if name.endswith(".q_norm.weight"):
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q_NORM, bid), data_torch)
+            return
+
+        if name.endswith(".k_norm.weight"):
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K_NORM, bid), data_torch)
+            return
+
+        # 4. All other tensors — pass through normally
+        yield from super().modify_tensors(data_torch, name, bid)
+
+```
+     
+No weights were altered. Only the tensor name metadata was remapped.
+
+### 2. RoPE Scaling Factor Correction
+
+During conversion, the script raised this warning:
+
+```
+The explicitly set RoPE scaling factor (config.rope_parameters['factor'] = 8.0)
+does not match the ratio implicitly set by other parameters
+(implicit factor = max_position_embeddings / original_max_position_embeddings = 4.0).
+Using the explicit factor (8.0) in YaRN. This may cause unexpected behaviour.
+```
+
+The implicit factor (4.0) is mathematically derived from the model's own position embedding settings. The explicit `8.0` in the upstream config appears to be an authoring error. To produce a consistent and correctly-behaving GGUF, **the factor was corrected from `8.0` to `4.0`** in `config.json` before conversion.
+
+This means the effective context window for these GGUFs reflects the 4.0× YaRN scaling, not 8.0×. If Flower Labs corrects the upstream config, a re-conversion would be straightforward.
+
+## License
+
+The original Lizzy-7B model is released under **Apache 2.0** by Flower Labs. These quants inherit that license.
+
+## Links
+
+- [Original Model: FlowerLabs/Lizzy-7B](https://huggingface.co/flwrlabs/Lizzy-7B)
+- [Flower Labs](https://flower.ai)
+- [llama.cpp](https://github.com/ggerganov/llama.cpp)
+
+### About Me
+
+This GGUF port was completed by **Anshuman Singh**.
+
+* **GitHub:** [github.com/SolusOps](https://github.com/solusops)
+* **LinkedIn:** [linkedin.com/in/anshumansingh2023](https://www.linkedin.com/in/anshumansingh2023/)
+
+If this port helped your local deployment, feel free to connect!

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