Text Generation
Transformers
Safetensors
MLX
code
llama
fill-in-the-middle
multi-token-prediction
speculative-decoding
apple-silicon
text-generation-inference
Instructions to use philipjohnbasile/wisp-coder-110m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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
- Atomic Chat
| { | |
| "bins": [ | |
| "8_token_ish", | |
| "16_token_ish", | |
| "32_token_ish", | |
| "64_token_ish" | |
| ], | |
| "contract_id": "wisp-short-hole-fim-competitive-v1", | |
| "descriptive_intervals": { | |
| "Qwen__Qwen2.5-Coder-0.5B": { | |
| "16_token_ish": [ | |
| 0.038461538461538464, | |
| 0.08875739644970414 | |
| ], | |
| "32_token_ish": [ | |
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| 0.02416918429003021 | |
| ], | |
| "64_token_ish": [ | |
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| 0.012468827930174564 | |
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| "8_token_ish": [ | |
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| }, | |
| "bigcode__tiny_starcoder_py": { | |
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| ], | |
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| }, | |
| "codeparrot__codeparrot-small": { | |
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| "32_token_ish": [ | |
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| "8_token_ish": [ | |
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| "ibm-granite__granite-4.0-350m-base": { | |
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| "wisp": { | |
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| }, | |
| "method": "paired nonparametric repository cluster bootstrap", | |
| "model_replicate_macro_means": { | |
| "Qwen__Qwen2.5-Coder-0.5B": { | |
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| "wisp": { | |
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| "mean": 0.010161879398330735 | |
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| "models": [ | |
| "Qwen__Qwen2.5-Coder-0.5B", | |
| "bigcode__tiny_starcoder_py", | |
| "codeparrot__codeparrot-small", | |
| "ibm-granite__granite-4.0-350m-base", | |
| "wisp" | |
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| "n_repositories": 24, | |
| "paired_deltas": { | |
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| "bigcode__tiny_starcoder_py": { | |
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| "repositories": [ | |
| "0xPlaygrounds/rig", | |
| "D4Vinci/Scrapling", | |
| "DataWithBaraa/sql-data-warehouse-project", | |
| "DietrichGebert/ponytail", | |
| "EpicGames/raddebugger", | |
| "Fission-AI/OpenSpec", | |
| "JuliusBrussee/caveman", | |
| "Netflix/maestro", | |
| "OpenCut-app/OpenCut", | |
| "TecharoHQ/anubis", | |
| "asg017/sqlite-vec", | |
| "browser-use/browser-use", | |
| "cloudwego/eino", | |
| "dockur/windows", | |
| "henrygd/beszel", | |
| "j178/prek", | |
| "jackwener/OpenCLI", | |
| "microsoft/RustTraining", | |
| "microsoft/markitdown", | |
| "modelcontextprotocol/java-sdk", | |
| "nicbarker/clay", | |
| "steipete/agent-scripts", | |
| "unitycatalog/unitycatalog", | |
| "upstash/context7" | |
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| "samples": 10000, | |
| "schema_version": 1, | |
| "seed": 20260731 | |
| } | |