Instructions to use SolusOps/Lizzy-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SolusOps/Lizzy-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SolusOps/Lizzy-7B-GGUF",
	filename="lizzy-7b-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SolusOps/Lizzy-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SolusOps/Lizzy-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SolusOps/Lizzy-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SolusOps/Lizzy-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SolusOps/Lizzy-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SolusOps/Lizzy-7B-GGUF:Q4_K_M

Ollama
How to use SolusOps/Lizzy-7B-GGUF with Ollama:
```
ollama run hf.co/SolusOps/Lizzy-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use SolusOps/Lizzy-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SolusOps/Lizzy-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SolusOps/Lizzy-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SolusOps/Lizzy-7B-GGUF to start chatting

How to use SolusOps/Lizzy-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SolusOps/Lizzy-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SolusOps/Lizzy-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SolusOps/Lizzy-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SolusOps/Lizzy-7B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SolusOps/Lizzy-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/SolusOps/Lizzy-7B-GGUF:Q4_K_M
```

Lemonade

How to use SolusOps/Lizzy-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SolusOps/Lizzy-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lizzy-7B-GGUF-Q4_K_M

List all available models

lemonade list

Lizzy-7B-GGUF / README.md

SolusOps

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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!