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
| # | |
| # 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 | |