Instructions to use laion/snowball-67b-a2b-sft-s3-opencode-step1888 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use laion/snowball-67b-a2b-sft-s3-opencode-step1888 with Transformers:

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

pipe = pipeline("text-generation", model="laion/snowball-67b-a2b-sft-s3-opencode-step1888")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("laion/snowball-67b-a2b-sft-s3-opencode-step1888", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use laion/snowball-67b-a2b-sft-s3-opencode-step1888 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "laion/snowball-67b-a2b-sft-s3-opencode-step1888"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "laion/snowball-67b-a2b-sft-s3-opencode-step1888",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/laion/snowball-67b-a2b-sft-s3-opencode-step1888

SGLang

How to use laion/snowball-67b-a2b-sft-s3-opencode-step1888 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 "laion/snowball-67b-a2b-sft-s3-opencode-step1888" \
    --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": "laion/snowball-67b-a2b-sft-s3-opencode-step1888",
		"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 "laion/snowball-67b-a2b-sft-s3-opencode-step1888" \
        --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": "laion/snowball-67b-a2b-sft-s3-opencode-step1888",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use laion/snowball-67b-a2b-sft-s3-opencode-step1888 with Docker Model Runner:
```
docker model run hf.co/laion/snowball-67b-a2b-sft-s3-opencode-step1888
```

Snowball 67B-A2B SFT Stage 3: OpenCode

This is the BF16 Hugging Face/vLLM export of the OpenCode stage in Marin's ordered Chat → Thinking → OpenCode SFT campaign. It was trained on OpenCode trajectories and should be evaluated with an OpenCode harness.

Training provenance

Initialization: final Stage 2 Thinking checkpoint at step 630
Data: tokenized/grug-a2b-agentic-sft-eot@2026.08.05, using the restored fixed-EOT path from marin-community/marin#8171
Data lineage: 29 OpenCode trace datasets generated with a Qwen3.5-122B teacher
Schedule: five token epochs, 1,888 steps, sequence length 32,768, global batch size 64
Final loss: approximately 0.201
Native checkpoint: s3://marin-us-east-02a/marin/grug/snowball_step105149_sft_s3_agentic_eot_5ep/2026.08.13.1/checkpoints/step-1888/
Recipe source: marin-community/marin#8172 at 7b1ea1dc07
Runtime source: the recipe above plus distributed checkpoint writing from marin-community/marin#8140 and a two-hour checkpoint barrier configured for this run
Experiment: marin-community/marin#8225

The export preserves the Marin tokenizer and Delphi special tokens. It is not the Nemotron-Terminal/Terminus-2 Stage 3 described in the successful control from marin-community/marin#7743.

Downloads last month: -

Safetensors

Model size

67B params

Tensor type

BF16