Instructions to use keisoft/kei_m2_02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keisoft/kei_m2_02 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="keisoft/kei_m2_02")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("keisoft/kei_m2_02") model = AutoModelForCausalLM.from_pretrained("keisoft/kei_m2_02") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use keisoft/kei_m2_02 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "keisoft/kei_m2_02" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "keisoft/kei_m2_02", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/keisoft/kei_m2_02
- SGLang
How to use keisoft/kei_m2_02 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 "keisoft/kei_m2_02" \ --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": "keisoft/kei_m2_02", "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 "keisoft/kei_m2_02" \ --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": "keisoft/kei_m2_02", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use keisoft/kei_m2_02 with Docker Model Runner:
docker model run hf.co/keisoft/kei_m2_02
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b4e5617 | 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 | {
"_name_or_path": "baxtos/barson10-7",
"architectures": [
"StableLmForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 100257,
"eos_token_id": 100257,
"hidden_act": "silu",
"hidden_dropout": 0.0,
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 5632,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 4096,
"model_type": "stablelm",
"num_attention_heads": 32,
"num_hidden_layers": 24,
"num_key_value_heads": 32,
"pad_token_id": 100257,
"partial_rotary_factor": 0.25,
"qk_layernorm": false,
"rope_scaling": null,
"rope_theta": 10000,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.40.2",
"use_cache": false,
"use_parallel_residual": false,
"use_qkv_bias": true,
"vocab_size": 100352
}
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