Instructions to use XythicK/Lovelace-1-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XythicK/Lovelace-1-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XythicK/Lovelace-1-7B-GGUF",
	filename="lovelace-1-7b-q2_k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use XythicK/Lovelace-1-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XythicK/Lovelace-1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf XythicK/Lovelace-1-7B-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 XythicK/Lovelace-1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf XythicK/Lovelace-1-7B-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 XythicK/Lovelace-1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf XythicK/Lovelace-1-7B-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 XythicK/Lovelace-1-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XythicK/Lovelace-1-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/XythicK/Lovelace-1-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use XythicK/Lovelace-1-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XythicK/Lovelace-1-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XythicK/Lovelace-1-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/XythicK/Lovelace-1-7B-GGUF:Q4_K_M

Ollama
How to use XythicK/Lovelace-1-7B-GGUF with Ollama:
```
ollama run hf.co/XythicK/Lovelace-1-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use XythicK/Lovelace-1-7B-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 XythicK/Lovelace-1-7B-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 XythicK/Lovelace-1-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XythicK/Lovelace-1-7B-GGUF to start chatting

Docker Model Runner
How to use XythicK/Lovelace-1-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/XythicK/Lovelace-1-7B-GGUF:Q4_K_M
```

Lemonade

How to use XythicK/Lovelace-1-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XythicK/Lovelace-1-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lovelace-1-7B-GGUF-Q4_K_M

List all available models

lemonade list

Lovelace-1-7B

A research-oriented code language model focused on realistic software reasoning

Model Summary

Lovelace-1-7B is a 7-billion parameter, code-focused large language model based on bigcode/starcoder2-7b.

It is part of the Lovelace model family, which focuses on building scalable, engineering-aligned coding models intended for long-term use in tooling, agentic systems, and research environments.

Rather than optimising for short-term benchmarks, Lovelace prioritises correctness, constraint awareness, and system-level reasoning.

Model Family

Model	Base Model	Parameters	Status
Lovelace-1-3B	StarCoder2-3B	3B	Released
Lovelace-1-7B	StarCoder2-7B	7B	Released
Lovelace-1-15B	Planned	15B	Planned

All Lovelace models are designed to remain interface-compatible with the Lovelace Code runtime.

Architecture

Base architecture: Transformer (decoder-only)
Foundation model: StarCoder2-7B
Training paradigm: Continued pretraining and alignment for code-centric tasks
Modalities: Text (code and natural language)
Tokenisation: Inherited from StarCoder2

The architectural design closely follows StarCoder2-7B to preserve its strong multilingual and multi-language coding capabilities, while enabling future extensibility.

Intended Capabilities

Although formal benchmarks are not yet published, Lovelace-1-7B is designed for:

Code generation and completion across multiple programming languages
Code refactoring and explanation
Debugging and error localisation
API usage reasoning and software design discussion
Identifying infeasible or unrealistic engineering requests and responding with viable alternatives

The model is explicitly tuned to avoid hallucinated implementations, preferring transparent limitations and constructive guidance.

Lovelace Code Library

Lovelace-1-7B is intended to be used alongside Lovelace Code, a companion library providing:

Structured coding prompts and system templates
Long-request handling and staged generation
Guardrails for non-computable or impractical tasks
Integration points for execution, tooling, and agent frameworks

Current development focuses on stability for long requests, including multi-file generation and iterative refinement workflows.

Evaluation

At present:

No public benchmark results are released
Internal evaluation focuses on qualitative correctness, coherence under long prompts, and tool-aligned behaviour

Formal evaluation and transparent reporting are planned future work.

Limitations

Long-context stability is still under active development
No vision or multimodal support at this stage
Performance characteristics may differ from StarCoder2-7B depending on downstream usage

Users should evaluate the model carefully before deploying in production or safety-critical environments.

Roadmap

Planned improvements include:

Improved long-context stability in Lovelace Code
Release of Lovelace-1-15B
Vision-language support (code + visual inputs)
Public benchmarks and technical reporting
Deeper integration with agentic and execution-based systems

Intended Use

Lovelace-1-7B is suitable for:

Research into code-focused LLM behaviour
Developer tooling and agent-based coding systems
Educational and exploratory programming assistance

It is not intended for autonomous execution or high-risk domains without additional safeguards.

Acknowledgements

Lovelace-1-7B builds directly on the work of the BigCode project, specifically starcoder2-7b.

The Lovelace project draws inspiration from modern open-weight research releases and large-scale industrial coding systems.

Licence

Please refer to the licence of the underlying StarCoder2-7B model.

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GGUF

Model size

7B params

Architecture

starcoder2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for XythicK/Lovelace-1-7B-GGUF

Base model

bigcode/starcoder2-7b

Finetuned

aayanmishra-ml/Lovelace-1-7B

Quantized

(3)

this model