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
```
Atomic Chat

wisp-coder-110m / evidence /receipts /final_validation_receipt.json

philipjohnbasile

Publish audited Wisp Coder 110M release

818282c verified 12 days ago

Raw

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	{
	"amendments": [
	{
	"amended_at": "2026-07-26T04:17:33Z",
	"kind": "observed-sampler-reset-registration",
	"previous_config_sha256": "c1b94316f669aff48ccb42ebd08b465e8d700cea6e94f75ccafbe646a1470704",
	"reason": "Before final validation, run 1's observed step-300 process recovery was recorded as sampler_reset_steps [300]. This changes no validation batch, setting, metric, or endpoint.",
	"registered_endpoint_changed": false
	},
	{
	"active_run1_process_or_checkpoint_config_changed": false,
	"amended_at": "2026-07-26T05:38:59Z",
	"attested_config_sha256": "f1683a7b00bf1b1e93654ebb366fa0072ba6092b0dbad10272e9353196c3532d",
	"kind": "post-build-shard-integrity-binding",
	"previous_config_sha256": "0e56d381e836ffbd32d0ba423335c076863afb9282278533b570dc9f757f1515",
	"reason": "Before final validation, the current attested run 1 config was bound to the post-build receipt that hashes every current shard and records visible-grammar normalization. The active trainer and its checkpoint retain the older in-memory launch config. The only current-file addition is data_integrity provenance metadata, which changes no validation batch, setting, metric, model input, or endpoint.",
	"registered_endpoint_changed": false,
	"run1_shard_integrity_receipt_sha256": "5831ecd4a471fbe07e19b212bc3de44bed0b0b6b456083e888f66802937bf471"
	}
	],
	"batch_manifest": {
	"batches": 100,
	"sequences": 800,
	"sha256": "3c1df4b9289354b8036121ceee9cda9381d18e071dd42d2290d02fa9ae29ff9c",
	"target_tokens": 1638400
	},
	"config": {
	"path": "config/run1.json",
	"sha256": "f1683a7b00bf1b1e93654ebb366fa0072ba6092b0dbad10272e9353196c3532d"
	},
	"data_index": {
	"path": "data/shards/index.json",
	"sha256": "862b1a9b7cc6c3c0d31299e21b352e2b736de767a99bf7fa38213d6c60fc0db0"
	},
	"instrument_version": 1,
	"registered_at": "2026-07-26T02:59:32Z",
	"schema_version": 1,
	"settings": {
	"batch_size": 8,
	"batches": 100,
	"bootstrap_samples": 4000,
	"bootstrap_seed": 0,
	"bootstrap_unit": "sampled_batch",
	"dtype": "bfloat16",
	"mtp_depth": 2,
	"seed": 7,
	"seq_len": 2048,
	"span": 2051,
	"split": "val"
	},
	"status": "final_validation_registered",
	"trained_checkpoint_step": 19073,
	"trained_run_name": "wisp-run1-110m-code",
	"validation_shard": {
	"path": "data/shards/val_0000.bin",
	"sha256": "b393a614b7e0e1b99dc702d0a71ec507b45aba0dd8cf4f3437c14a68f21148ca"
	}
	}