How to use poolside/Laguna-XS-2.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="poolside/Laguna-XS-2.1", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("poolside/Laguna-XS-2.1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("poolside/Laguna-XS-2.1", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
How to use poolside/Laguna-XS-2.1 with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "poolside/Laguna-XS-2.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "poolside/Laguna-XS-2.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/poolside/Laguna-XS-2.1
How to use poolside/Laguna-XS-2.1 with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "poolside/Laguna-XS-2.1" \ --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": "poolside/Laguna-XS-2.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "poolside/Laguna-XS-2.1" \ --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": "poolside/Laguna-XS-2.1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use poolside/Laguna-XS-2.1 with Docker Model Runner:
I tested it for coding, and it works well and does a good job.but it. but this model tends to rewrite the whole file instead of just editing it. The rewriting often causes new issues, hope it can be optimized in the future. thank for your good job.
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