Text Generation
Transformers
Safetensors
minspark
language-model
transformer
rope
gqa
custom_code
tiny
looped
slm
custom-architecture
custom-tokenizer
Instructions to use MinimaLabs/min-spark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MinimaLabs/min-spark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MinimaLabs/min-spark", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MinimaLabs/min-spark", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MinimaLabs/min-spark with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MinimaLabs/min-spark" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MinimaLabs/min-spark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MinimaLabs/min-spark
- SGLang
How to use MinimaLabs/min-spark 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 "MinimaLabs/min-spark" \ --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": "MinimaLabs/min-spark", "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 "MinimaLabs/min-spark" \ --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": "MinimaLabs/min-spark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MinimaLabs/min-spark with Docker Model Runner:
docker model run hf.co/MinimaLabs/min-spark
scrub internal project references from generate.py
Browse files- generate.py +2 -2
generate.py
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"""Raw bundled inference CLI for min-spark (no transformers dependency).
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-
Mirrors
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prefix once, truncate to the last max_seq_len tokens, effort -> loop count.
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This is the second, self-contained integration path; the Transformers path is
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modeling_minspark.py. Prefer the Transformers path unless you want zero
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@@ -36,7 +36,7 @@ def load_model(ckpt_path: str | None = None, device: str = "cpu") -> Meiosis:
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@torch.no_grad()
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def generate(model, tokenizer, prompt: str, *, loops: int, max_new: int,
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temperature: float, top_k: int, device: str):
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"""Yield decoded tokens one at a time (mirrors loader
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ids = [EOS_ID] + tokenizer.encode(prompt).ids
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for _ in range(max_new):
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ctx = ids[-model.config.max_seq_len:]
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"""Raw bundled inference CLI for min-spark (no transformers dependency).
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Mirrors the Space loader's generation loop exactly: EOS
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prefix once, truncate to the last max_seq_len tokens, effort -> loop count.
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This is the second, self-contained integration path; the Transformers path is
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modeling_minspark.py. Prefer the Transformers path unless you want zero
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@torch.no_grad()
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def generate(model, tokenizer, prompt: str, *, loops: int, max_new: int,
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temperature: float, top_k: int, device: str):
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"""Yield decoded tokens one at a time (mirrors the Space loader)."""
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ids = [EOS_ID] + tokenizer.encode(prompt).ids
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for _ in range(max_new):
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ctx = ids[-model.config.max_seq_len:]
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