Instructions to use philipjohnbasile/wisp-coder-110m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

wisp-coder-110m

File size: 831 Bytes

818282c

{
  "architecture": "wisp_recursive_shared_module",
  "hidden_state_stage": "trunk_pre_final_norm_residual",
  "mtp_depth_trained": 2,
  "mtp_layers": 1,
  "note": "One shared MTP module applied recursively, Qwen3-Next style. It consumes the trunk hidden state at position i and the embedding of the token at i+k, and predicts the token at i+k+1. The LM head is shared with the trunk, which ties both computations to one output projection but does not guarantee close distributions. The module contains a transformer block whose attention was trained under a causal mask over the whole window: at inference it must be given the sequence, not a single position.",
  "recursive": true,
  "requires_full_sequence_attention": true,
  "schema_version": 1,
  "shared_lm_head": true,
  "tensor_prefix": "mtp.",
  "trained_steps": 19073
}