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: 5,777 Bytes
818282c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | #!/usr/bin/env bash
#
# Everything that must hold before run 1 starts.
#
# Each of these exists because something in it was actually broken. The
# gradient accumulation held every micro batch live, resume died on the first
# optimizer step, sample.py had never met a live MLX install, and the MTP index
# arithmetic is the bug GOAL predicted would be the expensive one. Running the
# suite takes a few minutes. Discovering any of it on day four costs days.
#
# Usage:
# ./scripts/preflight.sh # full gate, audits data/shards
# SKIP_CORPUS=1 ./scripts/preflight.sh # code only, before the corpus exists
#
set -u
PY=${PY:-.venv/bin/python}
CONFIG=${CONFIG:-config/run1.json}
SKIP_CORPUS=${SKIP_CORPUS:-0}
pass=0
fail=0
declare -a FAILED
check() {
local name=$1; shift
printf '%-42s' "$name"
if "$@" >"/tmp/preflight_$(echo "$name" | tr -c 'a-zA-Z0-9' '_').log" 2>&1; then
echo "PASS"
pass=$((pass + 1))
else
echo "FAIL"
fail=$((fail + 1))
FAILED+=("$name")
fi
}
echo "=== wisp preflight ==="
echo
check "python files compile" $PY -m py_compile \
model.py data.py checkpoint_fs.py train.py sample.py \
scripts/test_data_contract.py \
scripts/test_checkpoint_transaction.py \
scripts/test_corpus_quality.py \
scripts/training_data_contract.py \
scripts/test_training_data_contract.py \
scripts/derive_no_fim.py scripts/test_derive_no_fim.py \
scripts/test_sampler_resume.py \
scripts/test_prepare_data.py \
scripts/e2_contract.py scripts/test_e2_contract.py \
scripts/train_tokenizer.py scripts/prepare_data.py scripts/corpus.py \
scripts/benchmark_tokenizer_vocab.py \
scripts/build_eval_holdout.py scripts/check_holdout_overlap.py \
scripts/check_eval_holdout.py \
scripts/eval_acceptance.py scripts/eval_pairs.py \
scripts/compare_acceptance.py scripts/compare_format_ablation.py \
scripts/eval_rollout.py \
scripts/rollout_metrics.py scripts/eval_validation.py \
scripts/validation_metrics.py scripts/release_audit.py \
scripts/export_hf.py \
scripts/export_quantized.py scripts/test_export_quantized.py \
scripts/publish_hf.py scripts/test_publish_hf.py \
scripts/mutation_audit.py \
scripts/hf_metadata.py scripts/summarize_training.py \
scripts/supervisor_state.py scripts/supervisor_heartbeat.py \
scripts/supervisor_fixture.py \
scripts/bench.py scripts/audit_corpus.py scripts/test_eval_pairs.py \
scripts/test_compare_acceptance.py \
scripts/test_compare_format_ablation.py scripts/test_rollout_metrics.py \
scripts/test_rollout_replay.py scripts/test_mtp_trace.py \
scripts/verify_rollout_replay.py scripts/test_verify_rollout_replay.py \
scripts/test_release_rollout_v3.py \
scripts/test_validation_metrics.py \
scripts/test_release_audit.py \
scripts/test_build_eval_holdout.py scripts/test_holdout_overlap.py \
scripts/test_tokenizer_vocab.py scripts/test_export_metadata.py \
scripts/test_summarize_training.py
check "holdout builder selection" $PY scripts/test_build_eval_holdout.py
check "eval pair construction" $PY scripts/test_eval_pairs.py
check "acceptance control comparison" $PY scripts/test_compare_acceptance.py
check "format-ablation comparison" $PY scripts/test_compare_format_ablation.py
check "adaptive rollout metrics" $PY scripts/test_rollout_metrics.py
check "rollout branch replay" $PY scripts/test_rollout_replay.py
check "MTP generation trace" $PY scripts/test_mtp_trace.py
check "independent rollout replay" $PY scripts/test_verify_rollout_replay.py
check "release rollout v3 audit" $PY scripts/test_release_rollout_v3.py
check "final validation metrics" $PY scripts/test_validation_metrics.py
check "final release audit" $PY scripts/test_release_audit.py
check "training data contract" $PY scripts/test_data_contract.py
check "atomic checkpoint transaction" $PY scripts/test_checkpoint_transaction.py
check "Hub corpus quality schema" $PY scripts/test_corpus_quality.py
check "training-data disclosure" $PY scripts/test_training_data_contract.py
check "deterministic no-FIM derivation" $PY scripts/test_derive_no_fim.py
check "training sampler resume" $PY scripts/test_sampler_resume.py
check "corpus build setup" $PY scripts/test_prepare_data.py
check "E2 format-ablation contract" $PY scripts/test_e2_contract.py
check "supervisor lifecycle" bash scripts/test_supervisor.sh
check "holdout overlap scanner" $PY scripts/test_holdout_overlap.py
check "tokenizer merge-prefix truncation" $PY scripts/test_tokenizer_vocab.py
check "Hugging Face export metadata" $PY scripts/test_export_metadata.py
check "rollback-aware training telemetry" $PY scripts/test_summarize_training.py
check "MTP index alignment" $PY scripts/test_mtp_indices.py
check "chunked cross entropy equivalence" $PY scripts/test_ce_chunk.py
check "native GQA attention equivalence" $PY scripts/test_gqa_attention.py
check "quantized-export comparison math" $PY scripts/test_export_quantized.py
check "Hugging Face publication gate" $PY scripts/test_publish_hf.py
check "checkpoint resume" $PY scripts/test_resume.py
if [ "$SKIP_CORPUS" = "0" ]; then
IDX=$($PY -c "import json;print(json.load(open('$CONFIG'))['data_index'])")
TOK=$($PY -c "import json;print(json.load(open('$CONFIG'))['tokenizer_path'])")
check "corpus audit" $PY scripts/audit_corpus.py --index "$IDX" --tokenizer "$TOK"
else
echo "corpus audit SKIPPED"
fi
echo
echo "=== $pass passed, $fail failed ==="
if [ $fail -gt 0 ]; then
echo
echo "failures:"
for f in "${FAILED[@]}"; do
echo " - $f (see /tmp/preflight_$(echo "$f" | tr -c 'a-zA-Z0-9' '_').log)"
done
echo
echo "DO NOT LAUNCH. Fix these first."
exit 1
fi
echo
echo "cleared for launch:"
echo " caffeinate -dims ./scripts/supervise.sh $CONFIG"
exit 0
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