Instructions to use jolovicdev/Lacuna-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jolovicdev/Lacuna-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jolovicdev/Lacuna-V1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jolovicdev/Lacuna-V1") model = AutoModelForCausalLM.from_pretrained("jolovicdev/Lacuna-V1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jolovicdev/Lacuna-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jolovicdev/Lacuna-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jolovicdev/Lacuna-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jolovicdev/Lacuna-V1
- SGLang
How to use jolovicdev/Lacuna-V1 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 "jolovicdev/Lacuna-V1" \ --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": "jolovicdev/Lacuna-V1", "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 "jolovicdev/Lacuna-V1" \ --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": "jolovicdev/Lacuna-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jolovicdev/Lacuna-V1 with Docker Model Runner:
docker model run hf.co/jolovicdev/Lacuna-V1
license: apache-2.0
base_model: ByteDance-Seed/Seed-Coder-8B-Base
library_name: transformers
tags:
- code
- code-completion
- code-editing
- fill-in-the-middle
- autocomplete
- lacuna
Lacuna V1
Experimental code edit completion model for numbered marker spans inside Seed-Coder FIM prompts.
Lacuna V1 predicts replacement code for marked edit regions. It is a completion model, not a chat model.
At A Glance
| Field | Value |
|---|---|
| Type | Completion model |
| Task | Code edit completion |
| Prompt style | Seed-Coder FIM + numbered markers |
| License | Apache-2.0 |
Marker Contract
Markers define the regions to replace.
| Region | Open marker | Close marker |
|---|---|---|
| 1 | <|marker_1|> |
<|marker_2|> |
| 2 | <|marker_3|> |
<|marker_4|> |
| N | <|marker_2N-1|> |
<|marker_2N|> |
The prompt contains the surrounding code context and marker placeholders. The completion starts at the first marker and returns the replacement span with markers included.
Typical Input
<[fim-suffix]>
return total;
}
<[fim-prefix]>
function sum(items) {
let total = 0;
for (const item of items) {
<|marker_1|><|marker_2|>
}
<[fim-middle]>
<|marker_1|>
Typical Output
<|marker_1|>
total += item.value;
<|marker_2|>
Multiple Regions
For multiple edited regions, the output keeps the same marker order:
<|marker_1|>
first replacement
<|marker_2|><|marker_3|>
second replacement
<|marker_4|>
Completion Settings
| Setting | Value |
|---|---|
temperature |
0 |
top_p |
1 |
max_tokens |
256 |
Use a larger max_tokens value for longer or multi-region edits.
Limitations
Lacuna V1 is experimental. It can produce incorrect code, incomplete replacements, or malformed marker spans. Validate output before applying edits automatically.
License
Apache-2.0.