Instructions to use CausalLM/miniG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CausalLM/miniG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CausalLM/miniG", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CausalLM/miniG", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CausalLM/miniG with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CausalLM/miniG" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CausalLM/miniG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CausalLM/miniG
- SGLang
How to use CausalLM/miniG 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 "CausalLM/miniG" \ --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": "CausalLM/miniG", "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 "CausalLM/miniG" \ --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": "CausalLM/miniG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CausalLM/miniG with Docker Model Runner:
docker model run hf.co/CausalLM/miniG
File size: 2,568 Bytes
b6e61a1 93f2b0f b6e61a1 93f2b0f b6e61a1 | 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 | from transformers import PretrainedConfig
class ChatGLMConfig(PretrainedConfig):
model_type = "chatglm"
def __init__(
self,
num_layers=28,
padded_vocab_size=65024,
hidden_size=4096,
ffn_hidden_size=13696,
kv_channels=128,
num_attention_heads=32,
seq_length=2048,
hidden_dropout=0.0,
classifier_dropout=None,
attention_dropout=0.0,
layernorm_epsilon=1e-5,
rmsnorm=True,
apply_residual_connection_post_layernorm=False,
post_layer_norm=True,
add_bias_linear=False,
add_qkv_bias=False,
bias_dropout_fusion=True,
multi_query_attention=False,
multi_query_group_num=1,
rope_ratio=1,
apply_query_key_layer_scaling=True,
attention_softmax_in_fp32=True,
fp32_residual_connection=False,
pre_seq_len=None,
prefix_projection=False,
boi_token_id=None,
eoi_token_id=None,
**kwargs
):
self.num_layers = num_layers
self.vocab_size = padded_vocab_size
self.padded_vocab_size = padded_vocab_size
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.kv_channels = kv_channels
self.num_attention_heads = num_attention_heads
self.seq_length = seq_length
self.hidden_dropout = hidden_dropout
self.classifier_dropout = classifier_dropout
self.attention_dropout = attention_dropout
self.layernorm_epsilon = layernorm_epsilon
self.rmsnorm = rmsnorm
self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
self.post_layer_norm = post_layer_norm
self.add_bias_linear = add_bias_linear
self.add_qkv_bias = add_qkv_bias
self.bias_dropout_fusion = bias_dropout_fusion
self.multi_query_attention = multi_query_attention
self.multi_query_group_num = multi_query_group_num
self.rope_ratio = rope_ratio
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
self.attention_softmax_in_fp32 = attention_softmax_in_fp32
self.fp32_residual_connection = fp32_residual_connection
self.pre_seq_len = pre_seq_len
self.prefix_projection = prefix_projection
self.boi_token_id = boi_token_id
self.eoi_token_id = eoi_token_id
super().__init__(**kwargs)
|