Text Generation
Transformers
Safetensors
English
gpt_oss
Mixture of Experts
mixture-of-experts
causal-lm
tinystories
tiny-model
validation
debug-model
mxfp4
e2m1
e8m0
Instructions to use shibatch/tinygptossmoe3m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibatch/tinygptossmoe3m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shibatch/tinygptossmoe3m")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shibatch/tinygptossmoe3m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shibatch/tinygptossmoe3m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shibatch/tinygptossmoe3m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shibatch/tinygptossmoe3m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shibatch/tinygptossmoe3m
- SGLang
How to use shibatch/tinygptossmoe3m 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/tinygptossmoe3m" \ --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/tinygptossmoe3m", "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/tinygptossmoe3m" \ --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/tinygptossmoe3m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shibatch/tinygptossmoe3m with Docker Model Runner:
docker model run hf.co/shibatch/tinygptossmoe3m
File size: 3,467 Bytes
2129f51 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | {
"embedding.weight": "BF16",
"block.0.attn.norm.scale": "BF16",
"block.0.attn.qkv.weight": "BF16",
"block.0.attn.qkv.bias": "BF16",
"block.0.attn.sinks": "BF16",
"block.0.attn.out.weight": "BF16",
"block.0.attn.out.bias": "BF16",
"block.0.mlp.norm.scale": "BF16",
"block.0.mlp.gate.weight": "BF16",
"block.0.mlp.gate.bias": "BF16",
"block.0.mlp.mlp1_weight.blocks": "FP4",
"block.0.mlp.mlp1_weight.scales": "UE8",
"block.0.mlp.mlp1_bias": "BF16",
"block.0.mlp.mlp2_weight.blocks": "FP4",
"block.0.mlp.mlp2_weight.scales": "UE8",
"block.0.mlp.mlp2_bias": "BF16",
"block.1.attn.norm.scale": "BF16",
"block.1.attn.qkv.weight": "BF16",
"block.1.attn.qkv.bias": "BF16",
"block.1.attn.sinks": "BF16",
"block.1.attn.out.weight": "BF16",
"block.1.attn.out.bias": "BF16",
"block.1.mlp.norm.scale": "BF16",
"block.1.mlp.gate.weight": "BF16",
"block.1.mlp.gate.bias": "BF16",
"block.1.mlp.mlp1_weight.blocks": "FP4",
"block.1.mlp.mlp1_weight.scales": "UE8",
"block.1.mlp.mlp1_bias": "BF16",
"block.1.mlp.mlp2_weight.blocks": "FP4",
"block.1.mlp.mlp2_weight.scales": "UE8",
"block.1.mlp.mlp2_bias": "BF16",
"block.2.attn.norm.scale": "BF16",
"block.2.attn.qkv.weight": "BF16",
"block.2.attn.qkv.bias": "BF16",
"block.2.attn.sinks": "BF16",
"block.2.attn.out.weight": "BF16",
"block.2.attn.out.bias": "BF16",
"block.2.mlp.norm.scale": "BF16",
"block.2.mlp.gate.weight": "BF16",
"block.2.mlp.gate.bias": "BF16",
"block.2.mlp.mlp1_weight.blocks": "FP4",
"block.2.mlp.mlp1_weight.scales": "UE8",
"block.2.mlp.mlp1_bias": "BF16",
"block.2.mlp.mlp2_weight.blocks": "FP4",
"block.2.mlp.mlp2_weight.scales": "UE8",
"block.2.mlp.mlp2_bias": "BF16",
"block.3.attn.norm.scale": "BF16",
"block.3.attn.qkv.weight": "BF16",
"block.3.attn.qkv.bias": "BF16",
"block.3.attn.sinks": "BF16",
"block.3.attn.out.weight": "BF16",
"block.3.attn.out.bias": "BF16",
"block.3.mlp.norm.scale": "BF16",
"block.3.mlp.gate.weight": "BF16",
"block.3.mlp.gate.bias": "BF16",
"block.3.mlp.mlp1_weight.blocks": "FP4",
"block.3.mlp.mlp1_weight.scales": "UE8",
"block.3.mlp.mlp1_bias": "BF16",
"block.3.mlp.mlp2_weight.blocks": "FP4",
"block.3.mlp.mlp2_weight.scales": "UE8",
"block.3.mlp.mlp2_bias": "BF16",
"block.4.attn.norm.scale": "BF16",
"block.4.attn.qkv.weight": "BF16",
"block.4.attn.qkv.bias": "BF16",
"block.4.attn.sinks": "BF16",
"block.4.attn.out.weight": "BF16",
"block.4.attn.out.bias": "BF16",
"block.4.mlp.norm.scale": "BF16",
"block.4.mlp.gate.weight": "BF16",
"block.4.mlp.gate.bias": "BF16",
"block.4.mlp.mlp1_weight.blocks": "FP4",
"block.4.mlp.mlp1_weight.scales": "UE8",
"block.4.mlp.mlp1_bias": "BF16",
"block.4.mlp.mlp2_weight.blocks": "FP4",
"block.4.mlp.mlp2_weight.scales": "UE8",
"block.4.mlp.mlp2_bias": "BF16",
"block.5.attn.norm.scale": "BF16",
"block.5.attn.qkv.weight": "BF16",
"block.5.attn.qkv.bias": "BF16",
"block.5.attn.sinks": "BF16",
"block.5.attn.out.weight": "BF16",
"block.5.attn.out.bias": "BF16",
"block.5.mlp.norm.scale": "BF16",
"block.5.mlp.gate.weight": "BF16",
"block.5.mlp.gate.bias": "BF16",
"block.5.mlp.mlp1_weight.blocks": "FP4",
"block.5.mlp.mlp1_weight.scales": "UE8",
"block.5.mlp.mlp1_bias": "BF16",
"block.5.mlp.mlp2_weight.blocks": "FP4",
"block.5.mlp.mlp2_weight.scales": "UE8",
"block.5.mlp.mlp2_bias": "BF16",
"norm.scale": "BF16",
"unembedding.weight": "BF16"
}
|