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 new

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

Pi new

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 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.

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

Original model: FlowerLabs/Lizzy-7B

Official Quants: 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. Refer to the original model card 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.

Model tree for SolusOps/Lizzy-7B-GGUF

Base model

flwrlabs/Lizzy-7B

Quantized

(1)

this model

SolusOps
/

Lizzy-7B-GGUF