Instructions to use aayanmishra-ml/Lovelace-1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aayanmishra-ml/Lovelace-1-3B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aayanmishra-ml/Lovelace-1-3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aayanmishra-ml/Lovelace-1-3B")
model = AutoModelForCausalLM.from_pretrained("aayanmishra-ml/Lovelace-1-3B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aayanmishra-ml/Lovelace-1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aayanmishra-ml/Lovelace-1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aayanmishra-ml/Lovelace-1-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aayanmishra-ml/Lovelace-1-3B

SGLang

How to use aayanmishra-ml/Lovelace-1-3B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aayanmishra-ml/Lovelace-1-3B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aayanmishra-ml/Lovelace-1-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aayanmishra-ml/Lovelace-1-3B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aayanmishra-ml/Lovelace-1-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use aayanmishra-ml/Lovelace-1-3B 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 aayanmishra-ml/Lovelace-1-3B 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 aayanmishra-ml/Lovelace-1-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aayanmishra-ml/Lovelace-1-3B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="aayanmishra-ml/Lovelace-1-3B",
    max_seq_length=2048,
)

Docker Model Runner
How to use aayanmishra-ml/Lovelace-1-3B with Docker Model Runner:
```
docker model run hf.co/aayanmishra-ml/Lovelace-1-3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lovelace-1-3B

A code-focused large language model for reliable, scalable software reasoning

Overview

Lovelace-1-3B is a 3-billion parameter coding-centric language model built on top of the bigcode/starcoder2-3b foundation model.

It is designed as the first release in the Lovelace family: a line of models focused on practical code generation, reasoning, and tooling, with an emphasis on long-term scalability, research cleanliness, and deployment stability.

Lovelace is developed with a research-first mindset: prioritising architectural soundness, future extensibility, and real-world usability over short-term leaderboard optimisation.

Model Family

Model	Parameters	Status
Lovelace-1-3B	3B	✅ Available
Lovelace-1-7B	7B	✅ Available
Lovelace-1-15B	15B	🚧 Planned

All models in the Lovelace family share a consistent design philosophy and are intended to be drop-in compatible with the Lovelace Code runtime and tooling stack.

Design Philosophy

Lovelace is guided by three core principles:

Engineering realism The model is expected to recognise infeasible requests, surface constraints clearly, and propose workable alternatives rather than hallucinating solutions.
Scalability over spectacle Training and design decisions prioritise long-term scale (larger models, longer contexts, multimodality) rather than short-term benchmark gains.
Tool-aligned coding intelligence Lovelace is designed to function as part of a broader coding system — not as an isolated chatbot.

Lovelace Code Library

The model is intended to be used alongside Lovelace Code, a companion library that provides:

Structured prompt interfaces for coding tasks
Execution-aware request handling
Support for long-running and multi-step code generation
Guardrails against unrealistic or non-computable requests

Ongoing work focuses on improving stability for long requests, including multi-file generation, extended reasoning chains, and iterative refinement workflows.

Capabilities

While formal benchmarks are not yet published, Lovelace-1-3B is trained and evaluated internally for:

Code generation and completion
Code explanation and refactoring
Debugging and error analysis
API and library usage reasoning
High-level system design discussion

The model is particularly tuned to respond sensibly under uncertainty, favouring correctness and clarity over speculative output.

Current Limitations

No public benchmark suite released yet
Context length stability for very long requests is still under active development
Vision-language capabilities are not yet supported

These limitations are explicitly acknowledged and form part of the near-term roadmap.

Roadmap

Planned future work includes:

Improved long-context stability in Lovelace Code
Release of the Lovelace-1-15B model
Vision support (code + visual inputs)
Transparent evaluation and benchmark reporting
Deeper tool and execution integration

Intended Use

Lovelace is designed for:

Research and experimentation in code-focused LLMs
Developer tooling and agentic coding systems
Education and structured programming assistance

It is not intended for safety-critical systems without further evaluation.

Acknowledgements

Lovelace-1-3B is based on the excellent work of the BigCode project, specifically starcoder2-3b.

The project is inspired by modern research-grade model releases, including OpenAI’s open-weight efforts and contemporary large-scale coding systems.

Licence

Please refer to the underlying base model and repository for licensing details. Additional terms may apply to the Lovelace Code library.

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Safetensors

Model size

3B params

Tensor type

F32

Model tree for aayanmishra-ml/Lovelace-1-3B

Base model

bigcode/starcoder2-3b

Finetuned

(50)

this model

Quantizations

2 models

Collection including aayanmishra-ml/Lovelace-1-3B

Lovelace-1

Collection

First Edition of the Lovelace Coding Family • 2 items • Updated Dec 25, 2025 • 1