Text Generation
Transformers
Safetensors
Vietnamese
sai
custom-code
vietnamese
causal-lm
custom_code
Instructions to use thongbuind/SAI_35M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thongbuind/SAI_35M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thongbuind/SAI_35M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("thongbuind/SAI_35M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use thongbuind/SAI_35M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thongbuind/SAI_35M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thongbuind/SAI_35M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thongbuind/SAI_35M
- SGLang
How to use thongbuind/SAI_35M 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 "thongbuind/SAI_35M" \ --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": "thongbuind/SAI_35M", "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 "thongbuind/SAI_35M" \ --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": "thongbuind/SAI_35M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thongbuind/SAI_35M with Docker Model Runner:
docker model run hf.co/thongbuind/SAI_35M
| import torch | |
| import torch.nn as nn | |
| class RotaryPositionalEmbedding(nn.Module): | |
| def __init__(self, head_dim: int, max_seq_len: int, base: int = 10_000): | |
| super().__init__() | |
| assert head_dim % 2 == 0, "head_dim phải chẵn" | |
| inv_freq = 1.0 / ( | |
| base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim) | |
| ) | |
| self.register_buffer("inv_freq", inv_freq, persistent=False) | |
| self._build_cache(max_seq_len) | |
| def _build_cache(self, seq_len: int): | |
| pos = torch.arange(seq_len, dtype=torch.float32) | |
| freqs = torch.outer(pos, self.inv_freq) # (seq_len, head_dim/2) | |
| emb = torch.cat([freqs, freqs], dim=-1) # (seq_len, head_dim) | |
| self.register_buffer("cos_cached", emb.cos(), persistent=False) | |
| self.register_buffer("sin_cached", emb.sin(), persistent=False) | |
| def get_cos_sin(self, positions: torch.Tensor): | |
| cos = self.cos_cached[positions][None, :, None, :] | |
| sin = self.sin_cached[positions][None, :, None, :] | |
| return cos, sin | |
| def _rotate_half(x: torch.Tensor) -> torch.Tensor: | |
| x1, x2 = x.chunk(2, dim=-1) | |
| return torch.cat((-x2, x1), dim=-1) | |
| def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor: | |
| return x * cos + RotaryPositionalEmbedding._rotate_half(x) * sin | |