Instructions to use 0labs-in/V1.3-CSD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0labs-in/V1.3-CSD with Transformers:

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

pipe = pipeline("text-generation", model="0labs-in/V1.3-CSD", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("0labs-in/V1.3-CSD", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 0labs-in/V1.3-CSD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0labs-in/V1.3-CSD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0labs-in/V1.3-CSD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0labs-in/V1.3-CSD

SGLang

How to use 0labs-in/V1.3-CSD 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 "0labs-in/V1.3-CSD" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0labs-in/V1.3-CSD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "0labs-in/V1.3-CSD" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0labs-in/V1.3-CSD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0labs-in/V1.3-CSD with Docker Model Runner:
```
docker model run hf.co/0labs-in/V1.3-CSD
```

V1.3-CSD

Sky v1.3 CSD is a 0labs research checkpoint trained with Cognitive Scaffolding Decay.

CSD curriculum:

Long scaffold examples for code understanding.
Medium bridge examples for reduced explanation.
Clean concise examples for daily professional coding use.

This repository contains a standalone saved inference checkpoint and tokenizer/runtime files.

Research Metrics

Training ran on an AMD MI300X.

Stage	Rows	LR	Train Loss
stage1_scaffold	915	5e-7	1.025
stage2_bridge	1121	5e-7	1.043
stage3_clean	677	4e-7	0.840

Quick private objective eval:

Model	Objective Score
Sky v1.3 5.5B production baseline	20 / 24
V1.3-CSD	22 / 24

These automatic scores are conservative checks. Rubric categories still need human or judge-model grading for paper-quality results.

Colab Loading Note

Use snapshot_download() into a local folder before loading. This avoids dynamic module import issues caused by dots in repository names.

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="0labs-in/V1.3-CSD",
    local_dir="/content/sky_v1_3_csd",
    repo_type="model",
)

Then load from /content/sky_v1_3_csd with trust_remote_code=True.

Downloads last month: 400

Safetensors

Model size

6B params

Tensor type

BF16