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
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| "sha256": "7203746fea5b3910bef0a80cc1f74394dd524f7d20faa0cf003c4bedea3e580e" | |
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| "sha256": "26b6a22f5fc7bea914ba436a9190c8eae8fd4607dd66313649294b5b04ece5ef" | |
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| "sha256": "2b3f32d8c71dc3d33d4f069b5707d160c1e84bd8c778e5807101a52f3086b464" | |
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| "sha256": "3a31107246bec500025a38079fd0b7f9deb61f5b8c3720056fc650108ee9ceb5" | |
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| "sha256": "5692f15944dcef9a8a64a96ea5b5888b39024de321c2bb9a302f1c522bc3ea12" | |
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| "sha256": "827ecc920fa6dd4cfe2922fde4d2289967c9e32759f368b466761302cf27b3c0" | |
| }, | |
| "format_audit_corpus_script": { | |
| "path": "evidence/source/audit_corpus.py", | |
| "sha256": "3e115fd82b89bdf2e74c9791ddbc1bfc4ac10300cca8c6ce943069a8656a2391" | |
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| "sha256": "f1683a7b00bf1b1e93654ebb366fa0072ba6092b0dbad10272e9353196c3532d" | |
| }, | |
| "format_checkpoint_script": { | |
| "path": "evidence/source/checkpoint_fs.py", | |
| "sha256": "96e951a223a557de8559730d73a229cea5432c39957918ff79f9e81a30f69e50" | |
| }, | |
| "format_corpus_script": { | |
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| "sha256": "05afba561a6258812d2baa802456c29c00d30955bec7f5c46389b90b2dad9e93" | |
| }, | |
| "format_data_script": { | |
| "path": "evidence/source/data.py", | |
| "sha256": "7a15626912afb93802b76450e5918a7e8d8c67aad295df4c96cde1129da1c094" | |
| }, | |
| "format_derivation_script": { | |
| "path": "evidence/source/derive_no_fim.py", | |
| "sha256": "fcf252eb4b248a08a1ffb7e3feacec54d040b459f44cb41b499d5baf7773ba09" | |
| }, | |
| "format_holdout": { | |
| "path": "sha256:c8450430853b60785cefb5dee24f7d4f01412db27af57b316b7c234bc42cd047", | |
| "sha256": "c8450430853b60785cefb5dee24f7d4f01412db27af57b316b7c234bc42cd047" | |
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| "format_model_script": { | |
| "path": "evidence/source/model.py", | |
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| "path": "evidence/source/prepare_data.py", | |
| "sha256": "dc9f11dd96eadcd65917b62bd7ac8c641266b99fd1225cb4cdfea0ef08654d30" | |
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| "format_run1_shard_integrity_receipt": { | |
| "path": "sha256:5831ecd4a471fbe07e19b212bc3de44bed0b0b6b456083e888f66802937bf471", | |
| "sha256": "5831ecd4a471fbe07e19b212bc3de44bed0b0b6b456083e888f66802937bf471" | |
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| "format_tokenizer": { | |
| "path": "evidence/inputs/tokenizer.code32k.json", | |
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| "path": "evidence/source/training_data_contract.py", | |
| "sha256": "093e80ece400ca9b4bfe307972eb7dabee79d8a920b569b5e360262ba0e204dd" | |
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| "format_training_data_receipt": { | |
| "path": "sha256:b6d1ad6d3b65b9c540ab572ce5f1053e91ec1d6afb95722075640d4864f24621", | |
| "sha256": "b6d1ad6d3b65b9c540ab572ce5f1053e91ec1d6afb95722075640d4864f24621" | |
| }, | |
| "format_training_script": { | |
| "path": "evidence/source/train.py", | |
| "sha256": "d194337982362a9b0728cbaae24f245170d83f673aa6ce3323ddec25ecfcef30" | |
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| "model_card_template": { | |
| "path": "evidence/documents/MODEL_CARD.release_audited.md", | |
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| "path": "evidence/receipts/eval_rollout_receipt_v3.json", | |
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| "sha256": "9d4c3c56a99eb97f75cb4366c922882b1c008bc327913a65beaa57673e25e412" | |
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| "sha256": "53214c7cc098a3d29548563730855e16876ae28a615d61db62002dc170d81b0e" | |
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| "training_data_data_document": { | |
| "path": "evidence/documents/DATA.md", | |
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| "training_data_model_card_template": { | |
| "path": "evidence/documents/MODEL_CARD.training_data_registered.md", | |
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| "path": "sha256:b6d1ad6d3b65b9c540ab572ce5f1053e91ec1d6afb95722075640d4864f24621", | |
| "sha256": "b6d1ad6d3b65b9c540ab572ce5f1053e91ec1d6afb95722075640d4864f24621" | |
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| "path": "sha256:4c4881109594917acfa177bd5c0092fa1b2fea0d55fab9ea43ee5616562cd876", | |
| "sha256": "4c4881109594917acfa177bd5c0092fa1b2fea0d55fab9ea43ee5616562cd876" | |
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| }, | |
| "audit_source_sha256": "b73ed79ce6f4219f38e1c3019e238ead34e54820e833ac6e2878464b4d89e701", | |
| "checkpoint": { | |
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| "created_at": "2026-08-03T15:18:44.009635+00:00", | |
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| "path": "local-path-redacted", | |
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| "repo_id": "philipjohnbasile/wisp-coder-110m" | |
| }, | |
| "external_verification": { | |
| "argmax_agreement": 1.0, | |
| "relative_delta_threshold": 0.01, | |
| "relative_max_abs_delta": 0.0013754346258788902, | |
| "toolchain": { | |
| "mlx": "0.32.0", | |
| "platform": "macOS-27.0-arm64-arm-64bit-Mach-O", | |
| "python": "3.14.6", | |
| "torch": "2.13.0", | |
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| } | |
| }, | |
| "format_ablation": { | |
| "arm_mean_acceptance": { | |
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| "run1_fim_shuf": 0.7643998256325721, | |
| "run1_l2r": 0.779159582555294, | |
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| "run2_fim_shuf": 0.683045190423727, | |
| "run2_l2r": 0.7791734331846237 | |
| }, | |
| "baseline_revision_evidence": { | |
| "kind": "local_huggingface_cache_refs_captured_after_run1_corpus_build", | |
| "limitation": "This is repository-revision evidence, not a per-document raw-corpus manifest for run 1. Source drift between the run 1 build and the captured cache refs cannot be ruled out." | |
| }, | |
| "depth": 2, | |
| "documents": 200, | |
| "instrument_version": 4, | |
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| "contract": { | |
| "bootstrap_samples": 4000, | |
| "bootstrap_seed": 0, | |
| "bootstrap_unit": "paired_target_document", | |
| "depth": 2, | |
| "metric": "(run1_fim_shuf_minus_l2r)_minus_(run2_fim_shuf_minus_l2r)", | |
| "positive_when": "ci95_lower_gt_0" | |
| }, | |
| "difference_in_differences": 0.08136848583817483, | |
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| "run2_fim_shuf_minus_l2r": -0.09612824276089671, | |
| "verdict": "POSITIVE" | |
| }, | |
| "publication_ready": true, | |
| "research_outcomes_are_not_release_gates": true, | |
| "runtime_code_evidence": { | |
| "kind": "matched_sampler_reset_with_post_build_shard_attestation", | |
| "limitation": "Run 1 did not record source-file hashes or sampler state in its pre-fix checkpoints. A post-build receipt now hashes all 52 current shard files and binds deterministic visible-grammar normalization, but it does not prove that the bytes were unchanged since training began or recover exact original units. The step-300 process recovery reset the legacy sampler, causing 78,643,200 scheduled token positions, 1.57 percent of the training budget, to replay earlier random windows. sampler_reset_steps [300] matches that reset timing in both arms. Later checkpoints preserve exact sampler RNG state. Exact sampled-example equivalence still cannot be proven." | |
| }, | |
| "schema_version": 1, | |
| "secondary_endpoint": { | |
| "ci95": [ | |
| -0.0025777602791786196, | |
| 0.012247835688292982 | |
| ], | |
| "contract": { | |
| "bootstrap_samples": 4000, | |
| "bootstrap_seed": 0, | |
| "bootstrap_unit": "paired_target_document", | |
| "depth": 2, | |
| "metric": "(run1_fim_minus_fim_shuf)_minus_(run2_fim_minus_fim_shuf)" | |
| }, | |
| "difference_in_differences": 0.004933806210756302, | |
| "run1_fim_minus_fim_shuf": 0.020464541018009208, | |
| "run2_fim_minus_fim_shuf": 0.015530734807252866, | |
| "verdict": "NULL: the interval includes zero" | |
| } | |
| }, | |
| "portable_evidence_bundle_required": true, | |
| "portable_projection": { | |
| "local_paths_replaced": true, | |
| "schema_version": 1, | |
| "source_release_audit_sha256": "aeecb3ba42b6e3d930c041b5a5a70a37a0e3873dcb247daf799166e1401003dd" | |
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| "rollout": { | |
| "branch_local_replay_valid": true, | |
| "cross_policy_trajectory_identical": true, | |
| "historical_instrument_only": false, | |
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| 0.0, | |
| 0.0 | |
| ], | |
| "comparison": "selected_adaptive_minus_selected_fixed", | |
| "difference": 0.0, | |
| "documents": 60, | |
| "fixed_policy": "fixed_d4", | |
| "metric": "accepted_drafts_per_verification", | |
| "verdict": "NULL: the interval includes 0" | |
| }, | |
| "quality_equivalent_to_greedy_ar": false, | |
| "secondary_draft_issued_proxy_endpoint": { | |
| "adaptive_policy": "adaptive_h0.7", | |
| "ci95": [ | |
| 0.0, | |
| 0.0 | |
| ], | |
| "comparison": "selected_adaptive_minus_selected_fixed", | |
| "difference": 0.0, | |
| "documents": 60, | |
| "fixed_policy": "fixed_d4", | |
| "metric": "drafts_issued_per_output_token", | |
| "verdict": "NULL: the interval includes 0" | |
| }, | |
| "secondary_draft_work_endpoint": { | |
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| "ci95": [ | |
| 0.0, | |
| 0.0 | |
| ], | |
| "comparison": "selected_adaptive_minus_selected_fixed", | |
| "difference": 0.0, | |
| "documents": 60, | |
| "fixed_policy": "fixed_d4", | |
| "metric": "draft_recursions_per_output_token", | |
| "verdict": "NULL: the interval includes 0" | |
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| 0.0, | |
| 0.0 | |
| ], | |
| "comparison": "selected_adaptive_minus_selected_fixed", | |
| "difference": 0.0, | |
| "documents": 60, | |
| "fixed_policy": "fixed_d4", | |
| "metric": "output_tokens_per_target_forward", | |
| "verdict": "NULL: the interval includes 0" | |
| }, | |
| "selected_adaptive": { | |
| "calibration_value": 3.026329118185736, | |
| "metric": "accepted_drafts_per_verification", | |
| "policy": "adaptive_h0.7", | |
| "tie_break": "first_in_registered_candidate_order" | |
| }, | |
| "selected_fixed": { | |
| "calibration_value": 3.026329118185736, | |
| "metric": "accepted_drafts_per_verification", | |
| "policy": "fixed_d4", | |
| "tie_break": "first_in_registered_candidate_order" | |
| } | |
| }, | |
| "rollout_verification": { | |
| "branch_quality_independently_verified": true, | |
| "certified_near_tie_tokens": 0, | |
| "exact_argmax_tokens": 38400, | |
| "output_tokens": 38400, | |
| "path": "sha256:9d4c3c56a99eb97f75cb4366c922882b1c008bc327913a65beaa57673e25e412", | |
| "policy_documents": 600, | |
| "provenance_scope": "Unsigned local attestation bound to a pushed pre-execution receipt commit and the registered source, inputs, checkpoint, and argv; it is not a signed external or trusted-execution witness.", | |
| "rollout_execution_reproduced": true, | |
| "sha256": "9d4c3c56a99eb97f75cb4366c922882b1c008bc327913a65beaa57673e25e412" | |
| }, | |
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| "main_perplexity": 4.004043347726933, | |
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| { | |
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| { | |
| "ci95": [ | |
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| } | |
| ], | |
| "target_tokens": 1638400 | |
| } | |
| } | |