Text Generation
Transformers
Safetensors
English
HelpingAI
3B
Emotionally Intelligent
conversational
custom_code
Instructions to use OEvortex/HelpingAI-3B-v2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OEvortex/HelpingAI-3B-v2.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OEvortex/HelpingAI-3B-v2.2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-3B-v2.2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OEvortex/HelpingAI-3B-v2.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OEvortex/HelpingAI-3B-v2.2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OEvortex/HelpingAI-3B-v2.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OEvortex/HelpingAI-3B-v2.2
- SGLang
How to use OEvortex/HelpingAI-3B-v2.2 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 "OEvortex/HelpingAI-3B-v2.2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OEvortex/HelpingAI-3B-v2.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "OEvortex/HelpingAI-3B-v2.2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OEvortex/HelpingAI-3B-v2.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OEvortex/HelpingAI-3B-v2.2 with Docker Model Runner:
docker model run hf.co/OEvortex/HelpingAI-3B-v2.2
| """ HelpingAI model configuration""" | |
| from transformers import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| class HelpingAIConfig(PretrainedConfig): | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| model_type = "HelpingAI" | |
| def __init__( | |
| self, | |
| vocab_size=50304, | |
| hidden_size=2560, | |
| intermediate_size=6912, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_key_value_heads=32, | |
| head_dim=256, | |
| hidden_act="silu", | |
| max_position_embeddings=4096, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| hidden_activation=None, | |
| rope_theta=10000, | |
| rope_pct=0.25, | |
| attention_bias=False, | |
| attention_dropout=0.0, | |
| num_experts_per_tok=2, | |
| num_local_experts=8, | |
| router_aux_loss_coef=0.02, | |
| output_router_logits=False, | |
| norm_eps=1.0e-5, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.head_dim = head_dim | |
| self.hidden_act = hidden_act | |
| self.hidden_activation = hidden_activation | |
| self.num_key_value_heads = num_key_value_heads | |
| self.initializer_range = initializer_range | |
| self.rms_norm_eps = rms_norm_eps | |
| self.use_cache = use_cache | |
| self.rope_theta = rope_theta | |
| self.attention_bias = attention_bias | |
| self.attention_dropout = attention_dropout | |
| self.num_experts_per_tok = num_experts_per_tok | |
| self.num_local_experts = num_local_experts | |
| self.router_aux_loss_coef = router_aux_loss_coef | |
| self.output_router_logits = output_router_logits | |
| self.rope_pct = rope_pct | |
| self.norm_eps = norm_eps | |
| super().__init__(**kwargs) | |