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 /inputs /run1_config.json

philipjohnbasile

Publish audited Wisp Coder 110M release

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	{
	"beta1": 0.9,
	"beta2": 0.95,
	"bias_correction": true,
	"ckpt_interval": 100,
	"compute_dtype": "bfloat16",
	"data_dir": "data/shards",
	"data_index": "data/shards/index.json",
	"data_integrity": {
	"receipt": "config/run1_shard_integrity_receipt.json",
	"role": "current",
	"sha256": "5831ecd4a471fbe07e19b212bc3de44bed0b0b6b456083e888f66802937bf471"
	},
	"dim": 768,
	"eps": 1e-08,
	"eval_interval": 500,
	"ffn_hidden": 2048,
	"fim_chunk": 1024,
	"fim_rate": 0.7,
	"grad_accum": 16,
	"grad_clip": 1.0,
	"log_interval": 10,
	"lr": 0.0006,
	"lr_min": 6e-05,
	"max_steps": 19073,
	"micro_batch": 8,
	"mtp_depth": 2,
	"mtp_layers": 1,
	"mtp_weight": 0.3,
	"n_heads": 12,
	"n_kv_heads": 4,
	"n_layers": 12,
	"norm_eps": 1e-05,
	"out_dir": "out/run1",
	"rope_theta": 100000.0,
	"run_name": "wisp-run1-110m-code",
	"sampler_reset_steps": [
	300
	],
	"seed": 1337,
	"seq_len": 2048,
	"snapshot_interval": 2000,
	"sources": [
	{
	"data_dir": "python",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.24
	},
	{
	"data_dir": "javascript",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.13
	},
	{
	"data_dir": "typescript",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.12
	},
	{
	"data_dir": "go",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.09
	},
	{
	"data_dir": "rust",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.09
	},
	{
	"data_dir": "java",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.08
	},
	{
	"data_dir": "c",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.05
	},
	{
	"data_dir": "shell",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.03
	},
	{
	"data_dir": "sql",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.02
	},
	{
	"data_dir": "markdown",
	"quality_gate": true,
	"repo": "bigcode/starcoderdata",
	"text_field": "content",
	"weight": 0.07
	},
	{
	"name": "sample-10BT",
	"quality_gate": true,
	"repo": "HuggingFaceFW/fineweb-edu",
	"text_field": "text",
	"weight": 0.08
	}
	],
	"tie_embeddings": true,
	"tokenizer_path": "tokenizer/code32k.json",
	"vocab_size": 32768,
	"warmup_steps": 1000,
	"weight_decay": 0.1
	}