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

File size: 1,459 Bytes

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---
license: other
library_name: transformers
pipeline_tag: text-generation
tags:
  - sky
  - 0labs
  - csd
  - cognitive-scaffolding-decay
  - coding
  - research
---

# V1.3-CSD

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

CSD curriculum:

1. Long scaffold examples for code understanding.
2. Medium bridge examples for reduced explanation.
3. 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.

```python
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`.