Text Generation
Transformers
PyTorch
code
mpt
Composer
MosaicML
llm-foundry
StreamingDatasets
custom_code
text-generation-inference
Instructions to use replit/replit-code-v1_5-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use replit/replit-code-v1_5-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="replit/replit-code-v1_5-3b", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("replit/replit-code-v1_5-3b", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("replit/replit-code-v1_5-3b", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use replit/replit-code-v1_5-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "replit/replit-code-v1_5-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "replit/replit-code-v1_5-3b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/replit/replit-code-v1_5-3b
- SGLang
How to use replit/replit-code-v1_5-3b 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 "replit/replit-code-v1_5-3b" \ --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": "replit/replit-code-v1_5-3b", "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 "replit/replit-code-v1_5-3b" \ --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": "replit/replit-code-v1_5-3b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use replit/replit-code-v1_5-3b with Docker Model Runner:
docker model run hf.co/replit/replit-code-v1_5-3b
File size: 578 Bytes
fe58961 b85a5dd fe58961 | 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 | {
"add_prefix_space": false,
"additional_special_tokens": [
"<fim_prefix>",
"<fim_middle>",
"<fim_suffix>",
"<fim_eot>",
"<fim_pad>",
"[INST]",
"[/INST]",
"<extra_id_2>",
"<extra_id_3>",
"<extra_id_4>",
"<extra_id_5>",
"<extra_id_6>",
"<extra_id_7>",
"<extra_id_8>",
"<extra_id_9>",
"<extra_id_10>"
],
"bos_token": "<|endoftext|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"model_max_length": 4096,
"tokenizer_class": "GPTNeoXTokenizer",
"unk_token": "<|endoftext|>"
} |