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
File size: 2,875 Bytes
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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
}
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