Instructions to use Klevin/PRIME-3B-v2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Klevin/PRIME-3B-v2.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Klevin/PRIME-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("Klevin/PRIME-3B-v2.2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Klevin/PRIME-3B-v2.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Klevin/PRIME-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": "Klevin/PRIME-3B-v2.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Klevin/PRIME-3B-v2.2
- SGLang
How to use Klevin/PRIME-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 "Klevin/PRIME-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": "Klevin/PRIME-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 "Klevin/PRIME-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": "Klevin/PRIME-3B-v2.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Klevin/PRIME-3B-v2.2 with Docker Model Runner:
docker model run hf.co/Klevin/PRIME-3B-v2.2
File size: 2,049 Bytes
c79c324 | 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 | """ 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)
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