Libraries Transformers How to use philipjohnbasile/wisp-coder-110m with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="philipjohnbasile/wisp-coder-110m") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("philipjohnbasile/wisp-coder-110m")
model = AutoModelForCausalLM.from_pretrained("philipjohnbasile/wisp-coder-110m", device_map="auto") MLX How to use philipjohnbasile/wisp-coder-110m with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("philipjohnbasile/wisp-coder-110m")
prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True) Notebooks Google Colab Kaggle Local Apps Settings LM Studio vLLM How to use philipjohnbasile/wisp-coder-110m with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "philipjohnbasile/wisp-coder-110m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "philipjohnbasile/wisp-coder-110m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Use Docker docker model run hf.co/philipjohnbasile/wisp-coder-110m SGLang How to use philipjohnbasile/wisp-coder-110m 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 "philipjohnbasile/wisp-coder-110m" \
--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": "philipjohnbasile/wisp-coder-110m",
"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 "philipjohnbasile/wisp-coder-110m" \
--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": "philipjohnbasile/wisp-coder-110m",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' MLX LM How to use philipjohnbasile/wisp-coder-110m with MLX LM:
Generate or start a chat session # Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "philipjohnbasile/wisp-coder-110m" --prompt "Once upon a time" Docker Model Runner How to use philipjohnbasile/wisp-coder-110m with Docker Model Runner:
docker model run hf.co/philipjohnbasile/wisp-coder-110m 
