Instructions to use litert-community/clef-flash-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/clef-flash-LiteRT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download contract.json from litert-community/clef-flash-LiteRT: direct link, hf CLI and curl.
- Browser
- Download file 21.3 kB
-
https://huggingface.co/litert-community/clef-flash-LiteRT/resolve/main/contract.json
- Command line
-
hf download hf://litert-community/clef-flash-LiteRT/contract.json
-
curl -L -o contract.json https://huggingface.co/litert-community/clef-flash-LiteRT/resolve/main/contract.json
21.3 kB
| { | |
| "what": "clef-flash on LiteRT: the contract between the graphs and a host", | |
| "model": { | |
| "repo": "Cloudflare/clef-flash", | |
| "revision": "f0b7d3cd65296abfe459e6c228b618f5d8b26d2f", | |
| "joint_schema_model_sha256": "0e304cf7c6500e8bb59bef7e2afd2c6373f82596dfb3b57d1aa93c175e2dc3a3", | |
| "hidden_size": 4096, | |
| "vocab_rows": 248320, | |
| "tie_word_embeddings": "false (embed_tokens and lm_head are two tables)" | |
| }, | |
| "host": { | |
| "package": "host/clef_litert", | |
| "version": "0.2.0", | |
| "requirements": "host/requirements-host.txt (Python 3.14.6, macOS 27.0): numpy, tokenizers, pillow and ai-edge-litert (imported when a graph is compiled); no torch, no transformers", | |
| "files_sha256": { | |
| "__init__.py": "4bc079245c456d5ef34b21c3cfd6614e367d5173a2fbf95d95a778279d9e80a2", | |
| "_erf.py": "cf6a894a8d65d069f64fb4f0f0175d969bd2947c31be89eb0992ab3435a4e66d", | |
| "errors.py": "43893807154cfa7822cdaabf2eb8db3650b9ccc615e2b4231f9437f527e94744", | |
| "head.py": "7fe0940018882ab7a83b12e78b884e194c332d5a7fcde9a93419f4bdec57f861", | |
| "host.py": "b4e39b9f525f2f7fd83ad29c8cb301acfcda3780c4484f577a16fdcd5a50627a", | |
| "pixels.py": "d87420187e422ff35299f5028236954fbbe63f6653e0d8a90a2ccfb0a500abf2", | |
| "positions.py": "44a86a2843c550fab76d6e9754518e07e1f142ba252ebe6e584825a2870915d4", | |
| "request.py": "991a3300bf9240d80b30a886f3d96cb77551840ed53d2af4221a5eaa0e118ab0", | |
| "response.py": "5658ca395deef7ee20c68e8244f5b1dd42af7c8021c6a8138acd685d89d91445", | |
| "runtime.py": "11ee9dc121b9c224a693d8c9eab244bde5b0bfb4baa10dcd9499167423f8ee7a", | |
| "tables.py": "6db98e0069162a8a0d9bbc56111f3676d382b34e0b2a1c4a86794f287fe2a521" | |
| }, | |
| "entry": "ClefLiteRT.from_dir(folder, accelerator='gpu' | 'cpu').decide(request, tile=448 | 256)", | |
| "steps": "host/clef_litert/__init__.py docstring 1-6; ClefHost.decide(request, row_graph, tower)" | |
| }, | |
| "folder": { | |
| "layout": { | |
| "row": "clef-flash_rowprefill_embeds_L{length}_fp16fc.tflite", | |
| "tower": "clef-flash_vision_tower_g{tile}_fp16fc.tflite", | |
| "tower_pos": "tables/vision_pos_g{tile}.safetensors", | |
| "embed_tokens": "tables/embed_tokens.safetensors", | |
| "lm_head": "tables/lm_head.safetensors", | |
| "joint_head": "head/joint_head.safetensors", | |
| "joint_head_config": "head/joint_head_config.json", | |
| "tokenizer": "tokenizer/tokenizer.json" | |
| }, | |
| "lengths": [ | |
| 256, | |
| 512, | |
| 1024, | |
| 2048, | |
| 4096 | |
| ], | |
| "tiles": [ | |
| 256, | |
| 448 | |
| ], | |
| "rule": "a request runs on the smallest present row graph that holds it; longer than the largest present one = RequestTooLong; one row graph compiled at a time, the towers stay compiled", | |
| "accelerators": { | |
| "gpu": "HardwareAccelerator.GPU, GpuOptions(enforce_f32=True) (Metal on a Mac)", | |
| "cpu": "HardwareAccelerator.CPU, CpuOptions(num_threads=8) (XNNPACK), no weight cache file" | |
| }, | |
| "not_supported": "the GPU's default precision (fp16 storage): the row graphs' norms overflow and the towers' rows are wrong (measured, not shipped)" | |
| }, | |
| "row_graphs": [ | |
| { | |
| "length": 256, | |
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| "files": { | |
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| "bytes": 19989325, | |
| "source": "the repository's own file, unchanged" | |
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| "source": "the repository's own file, unchanged (122 BF16 tensors)" | |
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| "metadata": null, | |
| "source": { | |
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| "data_sha256": "d87d444a38822588c6c1ed07dec4404ccd8d526f57d58565cbbbf60acc758afe" | |
| }, | |
| "made_by": "conversion/r5a_tables.py (clef_litert.copy_bf16_tensor): the checkpoint's bytes, new name", | |
| "role": "the row graph's embeds: the rows of the ids, widened to float32 by the host" | |
| }, | |
| "tables/lm_head.safetensors": { | |
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| "metadata": null, | |
| "source": { | |
| "file": "model-00001-of-00004.safetensors", | |
| "key": "lm_head.weight", | |
| "byte_range": [ | |
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| "data_sha256": "6d6ff4dbf261f833246d32e7c6088f87f4ab40ddb58516d90c931aa6ae61d5e7" | |
| }, | |
| "made_by": "conversion/r5a_tables.py (clef_litert.copy_bf16_tensor): the checkpoint's bytes, new name", | |
| "role": "the joint head's lexical vectors: the mean of the rows of each option span's ids" | |
| } | |
| }, | |
| "token_ids": { | |
| "vision_start": 248053, | |
| "vision_end": 248054, | |
| "image_pad": 248056, | |
| "processor_pad": 248044 | |
| }, | |
| "request": { | |
| "shape": "SystemOne-compatible request shape: {model: str, state: any JSON, questions: {id: {type, instructions, criteria}}, images: [one picture] (optional)}", | |
| "question_types": { | |
| "noul": 0, | |
| "choice": 1, | |
| "score": 2 | |
| }, | |
| "checks": "the author's systemone() checks, in order (request.validate_request), then the host's", | |
| "refusals": { | |
| "MissingModelOrState": "model is not a string or state is absent: 'model and state are required'", | |
| "NoQuestions": "questions is not a non-empty object: 'at least one question is required'", | |
| "BadQuestionType": "type not noul / choice / score: '<id>: type must be noul, choice, or score'", | |
| "EmptyCriteria": "choice or score without criteria: '<id>: criteria must not be empty'", | |
| "SchemaTooLong": "prefix + picture + schema + suffix > max_length (16384), encode_record's message", | |
| "EmptyRecord": "no ids or no questions, encode_record's message", | |
| "UnsupportedMedia": "host only: more than one picture, videos, media_kwargs, an unreadable picture or URL, a tile other than 256 / 448", | |
| "RequestTooLong": "host only: more than 4096 tokens (refused, never truncated)", | |
| "base": "every refusal is clef_litert.RequestError, a ValueError" | |
| } | |
| }, | |
| "ids": { | |
| "encode": "tokenizers.Tokenizer.from_file(tokenizer.json).encode(piece, add_special_tokens=False), pieces concatenated (the author's encode_record)", | |
| "prefix": "<|im_start|>system\nRead the complete state and schema. Decide every field jointly. Each answer must be exactly one of that field's allowed options.<|im_end|>\n<|im_start|>user\nSTATE:\n", | |
| "prefix_ids": 36, | |
| "suffix": "\n<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\nJOINT SCHEMA DECISIONS:", | |
| "suffix_ids": 18, | |
| "schema_header": "\n\nSCHEMA FIELDS:\n", | |
| "question_piece": "\\nFIELD {i}\\nID: {id}\\nTYPE: {type}\\nINSTRUCTION: [render(instructions or id)]\\nALLOWED OPTIONS:\\n + per option 'OPTION {j}: [render({option_id, description})]\\n' + 'END FIELD\\n' ([..] = the spans)", | |
| "render": "a string as is, else json.dumps(ensure_ascii=False, separators=(',', ':'), sort_keys=True)", | |
| "option_order": "noul (true, false); choice: ids sorted as strings; score: levels 0..n-1", | |
| "noul_default_criteria": { | |
| "true": "The proposition is true or the answer is yes.", | |
| "false": "The proposition is false or the answer is no." | |
| }, | |
| "picture_block": "<|vision_start|> + N x <|image_pad|> + <|vision_end|> + '\\n' right after the prefix (token_offset = the <|vision_start|> index)", | |
| "state_cut": "to max_length 16384 exactly as encode_record (the row graphs refuse past 4096 first)", | |
| "usage_input_tokens": "len(ids)" | |
| }, | |
| "row_graph": { | |
| "signature": "serving_default", | |
| "inputs": [ | |
| { | |
| "name": "embeds", | |
| "dtype": "float32", | |
| "shape": [ | |
| 1, | |
| "L", | |
| 4096 | |
| ], | |
| "meaning": "the bf16 embed_tokens rows of the ids widened to float32; the picture rows at the <|image_pad|> positions; 0 after the record" | |
| }, | |
| { | |
| "name": "valid", | |
| "dtype": "float32", | |
| "shape": [ | |
| 1, | |
| "L" | |
| ], | |
| "meaning": "1 on the record, 0 after it" | |
| }, | |
| { | |
| "name": "cos", | |
| "dtype": "float32", | |
| "shape": [ | |
| 1, | |
| "L", | |
| 64 | |
| ], | |
| "meaning": "M-RoPE cos of the record's position planes (positions.py)" | |
| }, | |
| { | |
| "name": "sin", | |
| "dtype": "float32", | |
| "shape": [ | |
| 1, | |
| "L", | |
| 64 | |
| ], | |
| "meaning": "M-RoPE sin" | |
| } | |
| ], | |
| "outputs": [ | |
| { | |
| "name": "hidden", | |
| "dtype": "float32", | |
| "shape": [ | |
| 1, | |
| "L", | |
| 4096 | |
| ], | |
| "meaning": "after the final RMSNorm, every position; the host reads rows 0..T-1" | |
| } | |
| ], | |
| "buckets": [ | |
| 256, | |
| 512, | |
| 1024, | |
| 2048, | |
| 4096 | |
| ], | |
| "bucket_rule": "the smallest L >= T; T > 4096 refused (RequestTooLong)", | |
| "padding": "right; embeds 0, valid 0; pad positions continue max + 1, + 2, ... on all three planes", | |
| "files": [ | |
| "clef-flash_rowprefill_embeds_L256_fp16fc.tflite", | |
| "clef-flash_rowprefill_embeds_L512_fp16fc.tflite", | |
| "clef-flash_rowprefill_embeds_L1024_fp16fc.tflite", | |
| "clef-flash_rowprefill_embeds_L2048_fp16fc.tflite", | |
| "clef-flash_rowprefill_embeds_L4096_fp16fc.tflite" | |
| ] | |
| }, | |
| "positions": { | |
| "planes": "text-only (i, i, i); picture of H x W merged tokens at token_offset P: i <= P -> (i, i, i), k-th picture token -> (P+1, P+1 + k // W, P+1 + k % W), i >= P+N+1 -> i - (N - max(H, W))", | |
| "rope": { | |
| "theta": 10000000.0, | |
| "rotary_dim": 64, | |
| "mrope_section": [ | |
| 11, | |
| 11, | |
| 10 | |
| ], | |
| "interleave": "j % 3 == 1 and j < 33 -> h, j % 3 == 2 and j < 30 -> w, else t", | |
| "tables": "angle = float32(inv_freq * pos); cos / sin of cat(angle, angle) in float64, rounded to float32" | |
| } | |
| }, | |
| "picture": { | |
| "tiles": { | |
| "256": { | |
| "merged_grid": [ | |
| 8, | |
| 8 | |
| ], | |
| "picture_tokens": 64, | |
| "patches": [ | |
| 256, | |
| 1536 | |
| ] | |
| }, | |
| "448": { | |
| "merged_grid": [ | |
| 14, | |
| 14 | |
| ], | |
| "picture_tokens": 196, | |
| "patches": [ | |
| 784, | |
| 1536 | |
| ] | |
| } | |
| }, | |
| "default_tile": 448, | |
| "preprocess": "PIL open -> convert('RGB') -> resize((tile, tile), BICUBIC) -> /255 -> (x - 0.5) / 0.5 (float64) -> float32 -> merge-block-major patches (C, T=2, 16, 16)", | |
| "tower_graph": "patches [1, 4 G^2, 1536] and the position rows pos [1, 4 G^2, 1152] float32 in -> picture rows [1, G^2, 4096] float32 out (tower_graphs)", | |
| "not_read": "the author's native resolution, more than one picture, videos, media_kwargs, URLs" | |
| }, | |
| "head": { | |
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