Text Generation
Transformers
Safetensors
English
deepseek-v2
multi-head-latent-attention
mla
mixture-of-experts
Mixture of Experts
tinystories
tiny-model
validation
debug-model
Instructions to use shibatch/tinydeepseekv2-3m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibatch/tinydeepseekv2-3m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shibatch/tinydeepseekv2-3m")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shibatch/tinydeepseekv2-3m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shibatch/tinydeepseekv2-3m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shibatch/tinydeepseekv2-3m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shibatch/tinydeepseekv2-3m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shibatch/tinydeepseekv2-3m
- SGLang
How to use shibatch/tinydeepseekv2-3m 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 "shibatch/tinydeepseekv2-3m" \ --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": "shibatch/tinydeepseekv2-3m", "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 "shibatch/tinydeepseekv2-3m" \ --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": "shibatch/tinydeepseekv2-3m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shibatch/tinydeepseekv2-3m with Docker Model Runner:
docker model run hf.co/shibatch/tinydeepseekv2-3m
File size: 246 Bytes
d093b93 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"backend": "tokenizers",
"bos_token": "<s>",
"eos_token": "</s>",
"extra_special_tokens": [
"<|im_start|>"
],
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<s>",
"tokenizer_class": "TokenizersBackend"
}
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