Instructions to use RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LFM2.5-Encoder-350M-Code-MXFP8-GPTQ RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP8-GPTQ
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Add files using upload-large-folder tool
Browse files- 1_Pooling/config.json +10 -0
- LICENSE +71 -0
- MODIFICATIONS.md +5 -0
- README.md +101 -0
- artifact_manifest.json +103 -0
- benchmarks/jina-code-calibrated-4k.json +187 -0
- benchmarks/lfm25-350m-bf16-4k.json +182 -0
- benchmarks/lfm25-350m-bf16-cuda-4k.json +191 -0
- benchmarks/lfm25-350m-gptq-mxfp4-4k.json +196 -0
- benchmarks/lfm25-350m-gptq-mxfp8-4k.json +196 -0
- benchmarks/lfm25-350m-gptq-mxfp8-vs-bf16-bootstrap.json +11 -0
- benchmarks/lfm25-350m-rtn-mxfp4-4k.json +196 -0
- benchmarks/lfm25-350m-rtn-mxfp8-4k.json +196 -0
- benchmarks/nomic-v1.5-4k.json +181 -0
- config.json +71 -0
- corpus_receipt.json +106 -0
- embedding_config.json +7 -0
- model.safetensors +3 -0
- modeling_lfm2_bidirectional.py +211 -0
- modules.json +14 -0
- quantization_report.json +1414 -0
- source_receipt.json +179 -0
- tokenizer.json +0 -0
- tokenizer_config.json +12 -0
- training_report.json +82 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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LICENSE
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MODIFICATIONS.md
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# Modifications by RESMP.DEV
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This is a derivative of the identified LiquidAI LFM2.5 Encoder checkpoint, not an official LiquidAI release. RESMP.DEV removed the masked-language-model head and contrastively fine-tuned the complete encoder body for code retrieval using the procedure and corpus hashes in `training_report.json`. The resulting weights are stored in BF16.
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RESMP.DEV activation-calibrated the BF16 weights with block-GPTQ and packed eligible linear layers as native group-32 MLX MXFP8. Exact settings and hashes are recorded in `quantization_report.json`.
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README.md
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---
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license: other
|
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license_name: lfm1.0
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license_link: LICENSE
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| 5 |
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base_model: LiquidAI/LFM2.5-Encoder-350M
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pipeline_tag: feature-extraction
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| 7 |
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library_name: mlx
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tags:
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| 9 |
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- code
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- embeddings
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- feature-extraction
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- mlx
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- mxfp8
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- gptq
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- quantized
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---
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# LFM2.5 Encoder 350M Code MXFP8-GPTQ
|
| 19 |
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|
| 20 |
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This is a modified RESMP.DEV research release derived from
|
| 21 |
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[`LiquidAI/LFM2.5-Encoder-350M`](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M) at revision `b886781f7c6f10ca9b7096e21b83e30a073c2f39`. It is
|
| 22 |
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not an official Liquid AI release. We removed the masked-language-model head and
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contrastively fine-tuned the full bidirectional encoder for multilingual code retrieval.
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| 24 |
+
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| 25 |
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## Quantization finding
|
| 26 |
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This is a research artifact, not an automatic recommendation to replace the BF16 model.
|
| 28 |
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Activation calibration is compared with matched native round-to-nearest quantization and
|
| 29 |
+
the complete machine-readable receipts are included so mobile and Apple-Silicon users
|
| 30 |
+
can evaluate the size, latency, memory, and quality tradeoff themselves.
|
| 31 |
+
|
| 32 |
+
## Held-out retrieval results
|
| 33 |
+
|
| 34 |
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All rows use the same untouched 6,995-pair multilingual test set, 1,200-character query
|
| 35 |
+
and 4,000-character passage caps, query token cap 512, and passage token cap 2,048.
|
| 36 |
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Higher is better. RTN is a matched quantization control; Nomic and Jina are external
|
| 37 |
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service baselines, not architecture-matched controls.
|
| 38 |
+
|
| 39 |
+
| Model | MRR | R@1 | R@5 | R@10 | NDCG@10 | Python MRR | TypeScript MRR | Artifact |
|
| 40 |
+
|---|---:|---:|---:|---:|---:|---:|---:|---:|
|
| 41 |
+
| LFM2.5 350M BF16 | 0.3705 | 0.2996 | 0.4422 | 0.5061 | 0.3963 | 0.7969 | 0.1917 | 713.7 MB |
|
| 42 |
+
| LFM2.5 350M calibrated MXFP4 | 0.1585 | 0.1169 | 0.1971 | 0.2317 | 0.1697 | 0.5795 | 0.0542 | 291.8 MB |
|
| 43 |
+
| LFM2.5 350M RTN MXFP4 | 0.0555 | 0.0422 | 0.0618 | 0.0773 | 0.0576 | 0.3204 | 0.0157 | 291.8 MB |
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| 44 |
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| LFM2.5 350M calibrated MXFP8 | 0.3710 | 0.3019 | 0.4430 | 0.5045 | 0.3962 | 0.7985 | 0.1903 | 435.5 MB |
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| 45 |
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| LFM2.5 350M RTN MXFP8 | 0.3684 | 0.2965 | 0.4427 | 0.5054 | 0.3945 | 0.7957 | 0.1932 | 435.4 MB |
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| 46 |
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| Nomic v1.5 service | 0.5439 | 0.4968 | 0.5954 | 0.6236 | 0.5595 | 0.9289 | 0.3617 | service |
|
| 47 |
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| Jina calibrated MXFP4 | 0.6645 | 0.6133 | 0.7221 | 0.7571 | 0.6832 | 0.9462 | 0.5057 | 1167.7 MB |
|
| 48 |
+
|
| 49 |
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A separate BF16 cross-runtime run on `NVIDIA GeForce RTX 3090 Ti` with PyTorch `2.13.0+cu130` produced MRR 0.3709, 616.2 queries/s, 139.1 passages/s, and 1109.0 MB peak CUDA allocation. CUDA throughput is reported separately and is not compared directly with Metal.
|
| 50 |
+
|
| 51 |
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A paired 10,000-sample bootstrap estimates calibrated MXFP8 minus BF16 MRR at +0.0005, with a 95% interval of [-0.0010, +0.0021]. A point estimate whose interval crosses zero is not presented as a
|
| 52 |
+
quality win.
|
| 53 |
+
|
| 54 |
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## Usage
|
| 55 |
+
|
| 56 |
+
```bash
|
| 57 |
+
git clone https://github.com/RESMP-DEV/calibrated-code-embeddings
|
| 58 |
+
cd calibrated-code-embeddings
|
| 59 |
+
uv sync --extra mlx
|
| 60 |
+
CODE_EMBEDDING_MODEL_PATH=/path/to/this-model code-embedding-serve --port 1235
|
| 61 |
+
```
|
| 62 |
+
|
| 63 |
+
The service exposes `POST /v1/embeddings`. It runs the bidirectional LFM2.5 body
|
| 64 |
+
directly with MLX; LM Studio is not required. Prefix retrieval queries with `query: `
|
| 65 |
+
and candidate code with `passage: ` when calling the model directly.
|
| 66 |
+
|
| 67 |
+
## Training and data receipts
|
| 68 |
+
|
| 69 |
+
Full-backbone symmetric in-batch InfoNCE training used 24,626 language-balanced pairs,
|
| 70 |
+
two epochs, batch size 32, learning rate 2e-5, temperature 0.05, and seed 17. The
|
| 71 |
+
training report records the NVIDIA RTX A6000 runtime and validation history.
|
| 72 |
+
|
| 73 |
+
- `train`: 42,626 rows, SHA-256 `426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629`
|
| 74 |
+
- `validation`: 5,319 rows, SHA-256 `9ac88b3138de4ca94c2ef3a87ccf19381fc76265c2bc9d65b4791983d0315096`
|
| 75 |
+
- `test`: 6,995 rows, SHA-256 `9ed10842a12132b6bfb5421df1e2f88dbcfbf6f6e960f36b22eb9ea6e3c72315`
|
| 76 |
+
- `calibration`: 4,096 rows, SHA-256 `ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9`
|
| 77 |
+
|
| 78 |
+
The corpus combines pinned CodeSearchNet data with pinned permissively licensed code
|
| 79 |
+
repositories. Exact and token 8-gram near-duplicates were removed with test-before-
|
| 80 |
+
validation-before-train precedence. See `corpus_receipt.json`, `source_receipt.json`,
|
| 81 |
+
`training_report.json`, `quantization_report.json` when present, `benchmarks/`, and
|
| 82 |
+
`artifact_manifest.json` for machine-readable evidence.
|
| 83 |
+
|
| 84 |
+
## License and attribution
|
| 85 |
+
|
| 86 |
+
The weights retain the LFM Open License v1.0 in `LICENSE`, including its attribution and
|
| 87 |
+
commercial-use conditions. `MODIFICATIONS.md` identifies RESMP.DEV's changes. The
|
| 88 |
+
[training and quantization workbench](https://github.com/RESMP-DEV/calibrated-code-embeddings)
|
| 89 |
+
is separately MIT licensed.
|
| 90 |
+
|
| 91 |
+
## Citation
|
| 92 |
+
|
| 93 |
+
```bibtex
|
| 94 |
+
@article{liquidAI2026Encoders,
|
| 95 |
+
author = {Liquid AI},
|
| 96 |
+
title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
|
| 97 |
+
journal = {Liquid AI Blog},
|
| 98 |
+
year = {2026},
|
| 99 |
+
note = {www.liquid.ai/blog/lfm2-5-encoders},
|
| 100 |
+
}
|
| 101 |
+
```
|
artifact_manifest.json
ADDED
|
@@ -0,0 +1,103 @@
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|
| 1 |
+
{
|
| 2 |
+
"artifact": "LFM2.5-Encoder-350M-Code-MXFP8-GPTQ",
|
| 3 |
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|
| 4 |
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"1_Pooling/config.json": {
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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"LICENSE": {
|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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| 19 |
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|
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
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|
| 25 |
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|
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|
| 28 |
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|
| 29 |
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|
| 33 |
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|
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|
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|
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|
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|
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|
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|
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| 79 |
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|
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
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|
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|
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|
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|
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|
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|
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|
| 97 |
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|
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
benchmarks/jina-code-calibrated-4k.json
ADDED
|
@@ -0,0 +1,187 @@
|
|
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|
| 1 |
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|
| 2 |
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|
| 3 |
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|
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|
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|
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|
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|
| 8 |
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|
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|
| 10 |
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|
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|
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|
| 13 |
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|
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|
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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},
|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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},
|
| 38 |
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|
| 39 |
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|
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|
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|
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|
| 43 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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benchmarks/lfm25-350m-bf16-4k.json
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"mrr": 0.3277453315914854,
|
| 175 |
+
"ndcg_at_10": 0.4153737804972049,
|
| 176 |
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"recall_at_1": 0.15384615384615385,
|
| 177 |
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"recall_at_10": 0.7692307692307693,
|
| 178 |
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"recall_at_5": 0.46153846153846156,
|
| 179 |
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"samples": 13
|
| 180 |
+
}
|
| 181 |
+
}
|
| 182 |
+
}
|
benchmarks/lfm25-350m-bf16-cuda-4k.json
ADDED
|
@@ -0,0 +1,191 @@
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|
| 1 |
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{
|
| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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},
|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 18 |
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|
| 19 |
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| 20 |
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| 21 |
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| 22 |
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| 23 |
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| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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},
|
| 39 |
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|
| 40 |
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|
| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 53 |
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| 54 |
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| 56 |
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| 62 |
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| 63 |
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| 64 |
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| 180 |
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| 181 |
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|
| 182 |
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| 189 |
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|
| 190 |
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|
| 191 |
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|
benchmarks/lfm25-350m-gptq-mxfp4-4k.json
ADDED
|
@@ -0,0 +1,196 @@
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benchmarks/lfm25-350m-gptq-mxfp8-4k.json
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@@ -0,0 +1,196 @@
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benchmarks/lfm25-350m-gptq-mxfp8-vs-bf16-bootstrap.json
ADDED
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@@ -0,0 +1,11 @@
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benchmarks/lfm25-350m-rtn-mxfp4-4k.json
ADDED
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@@ -0,0 +1,196 @@
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benchmarks/lfm25-350m-rtn-mxfp8-4k.json
ADDED
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@@ -0,0 +1,196 @@
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}
|
| 196 |
+
}
|
benchmarks/nomic-v1.5-4k.json
ADDED
|
@@ -0,0 +1,181 @@
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| 1 |
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{
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| 2 |
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| 14 |
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| 25 |
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| 26 |
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| 180 |
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| 181 |
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|
config.json
ADDED
|
@@ -0,0 +1,71 @@
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|
|
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|
|
|
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|
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|
|
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|
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|
|
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|
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|
|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
| 1 |
+
{
|
| 2 |
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"architectures": [
|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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| 14 |
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| 15 |
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|
| 17 |
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| 18 |
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| 19 |
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|
| 20 |
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| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
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|
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|
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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"conv",
|
| 33 |
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|
| 34 |
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"conv",
|
| 35 |
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"conv",
|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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"full_attention",
|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
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| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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"algorithm": "activation_calibrated_block_gptq"
|
| 70 |
+
}
|
| 71 |
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}
|
corpus_receipt.json
ADDED
|
@@ -0,0 +1,106 @@
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|
|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
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|
|
|
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|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"artifacts": {
|
| 3 |
+
"calibration.jsonl": {
|
| 4 |
+
"rows": 4096,
|
| 5 |
+
"sha256": "ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9"
|
| 6 |
+
},
|
| 7 |
+
"duplicates.jsonl": {
|
| 8 |
+
"rows": 1401,
|
| 9 |
+
"sha256": "2ddc5b1b0eaadffd9b9e7b7556f4a756c6d0b0857a509236ae3e090a9d17ad16"
|
| 10 |
+
},
|
| 11 |
+
"test.jsonl": {
|
| 12 |
+
"rows": 6995,
|
| 13 |
+
"sha256": "9ed10842a12132b6bfb5421df1e2f88dbcfbf6f6e960f36b22eb9ea6e3c72315"
|
| 14 |
+
},
|
| 15 |
+
"train.jsonl": {
|
| 16 |
+
"rows": 42626,
|
| 17 |
+
"sha256": "426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629"
|
| 18 |
+
},
|
| 19 |
+
"validation.jsonl": {
|
| 20 |
+
"rows": 5319,
|
| 21 |
+
"sha256": "9ac88b3138de4ca94c2ef3a87ccf19381fc76265c2bc9d65b4791983d0315096"
|
| 22 |
+
}
|
| 23 |
+
},
|
| 24 |
+
"codesearchnet": {
|
| 25 |
+
"languages": [
|
| 26 |
+
"go",
|
| 27 |
+
"java",
|
| 28 |
+
"javascript",
|
| 29 |
+
"php",
|
| 30 |
+
"python",
|
| 31 |
+
"ruby"
|
| 32 |
+
],
|
| 33 |
+
"local_root": "/Users/kearm/datasets/calibrated-code-embeddings/code-search-net",
|
| 34 |
+
"repository": "code-search-net/code_search_net",
|
| 35 |
+
"revision": "bd0cf261e357a3eb5c8fba490d23ec1a1cd59555"
|
| 36 |
+
},
|
| 37 |
+
"configuration": {
|
| 38 |
+
"calibration_rows": 4096,
|
| 39 |
+
"calibration_sampling": "deterministic_language_round_robin_v2",
|
| 40 |
+
"evaluation_precedence_during_dedup": [
|
| 41 |
+
"test",
|
| 42 |
+
"validation",
|
| 43 |
+
"train"
|
| 44 |
+
],
|
| 45 |
+
"near_duplicate_threshold": 0.82,
|
| 46 |
+
"ngram_size": 8,
|
| 47 |
+
"per_repo_language": 3000,
|
| 48 |
+
"test": 750,
|
| 49 |
+
"train": 6000,
|
| 50 |
+
"validation": 750
|
| 51 |
+
},
|
| 52 |
+
"counts": {
|
| 53 |
+
"test/c/pinned_repository": 95,
|
| 54 |
+
"test/cpp/pinned_repository": 750,
|
| 55 |
+
"test/cuda/pinned_repository": 1,
|
| 56 |
+
"test/go/codesearchnet": 750,
|
| 57 |
+
"test/java/codesearchnet": 268,
|
| 58 |
+
"test/java/pinned_repository": 482,
|
| 59 |
+
"test/javascript/codesearchnet": 332,
|
| 60 |
+
"test/javascript/pinned_repository": 418,
|
| 61 |
+
"test/php/codesearchnet": 750,
|
| 62 |
+
"test/python/codesearchnet": 631,
|
| 63 |
+
"test/python/pinned_repository": 119,
|
| 64 |
+
"test/ruby/codesearchnet": 750,
|
| 65 |
+
"test/rust/pinned_repository": 750,
|
| 66 |
+
"test/shell/pinned_repository": 6,
|
| 67 |
+
"test/swift/pinned_repository": 1,
|
| 68 |
+
"test/toml/pinned_repository": 129,
|
| 69 |
+
"test/typescript/pinned_repository": 750,
|
| 70 |
+
"test/yaml/pinned_repository": 13,
|
| 71 |
+
"train/c/pinned_repository": 389,
|
| 72 |
+
"train/cpp/pinned_repository": 268,
|
| 73 |
+
"train/cuda/pinned_repository": 1,
|
| 74 |
+
"train/go/codesearchnet": 6000,
|
| 75 |
+
"train/java/codesearchnet": 6000,
|
| 76 |
+
"train/javascript/codesearchnet": 4939,
|
| 77 |
+
"train/javascript/pinned_repository": 1061,
|
| 78 |
+
"train/php/codesearchnet": 6000,
|
| 79 |
+
"train/python/codesearchnet": 5984,
|
| 80 |
+
"train/python/pinned_repository": 16,
|
| 81 |
+
"train/ruby/codesearchnet": 6000,
|
| 82 |
+
"train/rust/pinned_repository": 2980,
|
| 83 |
+
"train/shell/pinned_repository": 2,
|
| 84 |
+
"train/toml/pinned_repository": 21,
|
| 85 |
+
"train/typescript/pinned_repository": 2945,
|
| 86 |
+
"train/yaml/pinned_repository": 20,
|
| 87 |
+
"validation/go/codesearchnet": 561,
|
| 88 |
+
"validation/go/pinned_repository": 189,
|
| 89 |
+
"validation/java/codesearchnet": 750,
|
| 90 |
+
"validation/javascript/codesearchnet": 580,
|
| 91 |
+
"validation/javascript/pinned_repository": 170,
|
| 92 |
+
"validation/php/codesearchnet": 750,
|
| 93 |
+
"validation/python/codesearchnet": 590,
|
| 94 |
+
"validation/python/pinned_repository": 160,
|
| 95 |
+
"validation/ruby/codesearchnet": 750,
|
| 96 |
+
"validation/toml/pinned_repository": 8,
|
| 97 |
+
"validation/typescript/pinned_repository": 750,
|
| 98 |
+
"validation/yaml/pinned_repository": 61
|
| 99 |
+
},
|
| 100 |
+
"input_pairs": 118039,
|
| 101 |
+
"local_source": "/Users/kearm/datasets/calibrated-code-embeddings/source-repos",
|
| 102 |
+
"repo_manifest": "/Users/kearm/AlphaHENG/contrib/calibrated-code-embeddings/sources/repos.json",
|
| 103 |
+
"schema_version": 1,
|
| 104 |
+
"selected_pairs": 54940,
|
| 105 |
+
"unique_pairs": 116638
|
| 106 |
+
}
|
embedding_config.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"pooling": "attention_mask_mean",
|
| 3 |
+
"normalize": true,
|
| 4 |
+
"dimensions": 1024,
|
| 5 |
+
"query_prefix": "query: ",
|
| 6 |
+
"passage_prefix": "passage: "
|
| 7 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:79288c8d909bf8e3fc6034bf307a06443ed32d67fc61e41155fc7dd4f62568f4
|
| 3 |
+
size 430661504
|
modeling_lfm2_bidirectional.py
ADDED
|
@@ -0,0 +1,211 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""LFM2 backbone with bidirectional attention + non-causal short-conv.
|
| 2 |
+
|
| 3 |
+
Wired into the HF repo via `auto_map` in config.json so that
|
| 4 |
+
|
| 5 |
+
AutoModel.from_pretrained(repo, trust_remote_code=True)
|
| 6 |
+
AutoModelForMaskedLM.from_pretrained(repo, trust_remote_code=True)
|
| 7 |
+
|
| 8 |
+
both return a model with the encoder-style patches already applied.
|
| 9 |
+
|
| 10 |
+
Supports `attn_implementation` in {"eager", "sdpa", "flash_attention_2"}:
|
| 11 |
+
|
| 12 |
+
eager/sdpa consume a 4D additive pad-only mask and reproduce the exact
|
| 13 |
+
training-time behavior; flash_attention_2 receives the 2D padding mask (or
|
| 14 |
+
None) and runs the kernel non-causally via `Lfm2Attention.is_causal = False`,
|
| 15 |
+
yielding outputs equivalent to the unpadded forward.
|
| 16 |
+
"""
|
| 17 |
+
|
| 18 |
+
from typing import Optional
|
| 19 |
+
|
| 20 |
+
import torch
|
| 21 |
+
import torch.nn as nn
|
| 22 |
+
import torch.nn.functional as F
|
| 23 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 24 |
+
from transformers.modeling_outputs import BaseModelOutput, MaskedLMOutput
|
| 25 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 26 |
+
from transformers.models.lfm2 import modeling_lfm2 as _lfm2_mod
|
| 27 |
+
from transformers.models.lfm2.configuration_lfm2 import Lfm2Config
|
| 28 |
+
from transformers.models.lfm2.modeling_lfm2 import (
|
| 29 |
+
Lfm2Attention,
|
| 30 |
+
Lfm2Model,
|
| 31 |
+
Lfm2PreTrainedModel,
|
| 32 |
+
Lfm2ShortConv,
|
| 33 |
+
apply_mask_to_padding_states,
|
| 34 |
+
)
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
def _bidirectional_mask(
|
| 38 |
+
config,
|
| 39 |
+
input_embeds: torch.Tensor = None,
|
| 40 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 41 |
+
cache_position: Optional[torch.LongTensor] = None,
|
| 42 |
+
past_key_values=None,
|
| 43 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 44 |
+
**kwargs,
|
| 45 |
+
) -> Optional[torch.Tensor]:
|
| 46 |
+
# transformers has renamed the embeds kwarg across versions
|
| 47 |
+
# (input_embeds <-> inputs_embeds); accept either to stay forward-compatible.
|
| 48 |
+
if input_embeds is None:
|
| 49 |
+
input_embeds = kwargs.get("inputs_embeds")
|
| 50 |
+
|
| 51 |
+
if config._attn_implementation == "flash_attention_2":
|
| 52 |
+
# FA2 only uses the 2D padding mask to unpad sequences; causality is
|
| 53 |
+
# controlled by `Lfm2Attention.is_causal` (set to False below).
|
| 54 |
+
if attention_mask is not None and not attention_mask.all():
|
| 55 |
+
return attention_mask
|
| 56 |
+
return None
|
| 57 |
+
|
| 58 |
+
device = input_embeds.device
|
| 59 |
+
dtype = input_embeds.dtype
|
| 60 |
+
bsz, q_len = input_embeds.shape[:2]
|
| 61 |
+
past = past_key_values.get_seq_length() if past_key_values is not None else 0
|
| 62 |
+
kv_len = past + q_len
|
| 63 |
+
|
| 64 |
+
mask = torch.zeros((bsz, 1, q_len, kv_len), device=device, dtype=dtype)
|
| 65 |
+
if attention_mask is not None:
|
| 66 |
+
cur_len = attention_mask.size(-1)
|
| 67 |
+
key_pad_flags = (attention_mask == 0).to(device=device, dtype=torch.float32)
|
| 68 |
+
pad_vec = torch.zeros((bsz, kv_len), device=device, dtype=torch.float32)
|
| 69 |
+
if cur_len > 0:
|
| 70 |
+
pad_vec[:, past:past + cur_len] = key_pad_flags * -1e9
|
| 71 |
+
mask = mask + pad_vec.to(dtype)[:, None, None, :]
|
| 72 |
+
return mask
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
def _noncausal_shortconv_forward(
|
| 76 |
+
self,
|
| 77 |
+
hidden_states: torch.Tensor,
|
| 78 |
+
past_key_values=None,
|
| 79 |
+
cache_position=None,
|
| 80 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 81 |
+
**kwargs,
|
| 82 |
+
) -> torch.Tensor:
|
| 83 |
+
x = apply_mask_to_padding_states(hidden_states, attention_mask)
|
| 84 |
+
|
| 85 |
+
BCx = self.in_proj(x).transpose(-1, -2)
|
| 86 |
+
B, C, x = BCx.chunk(3, dim=-2)
|
| 87 |
+
Bx = B * x
|
| 88 |
+
|
| 89 |
+
k = self.conv.weight.shape[-1]
|
| 90 |
+
pad = k // 2
|
| 91 |
+
conv_out = F.conv1d(
|
| 92 |
+
Bx, weight=self.conv.weight, bias=self.conv.bias,
|
| 93 |
+
stride=1, padding=pad, dilation=1, groups=Bx.shape[1],
|
| 94 |
+
)
|
| 95 |
+
if conv_out.shape[-1] > Bx.shape[-1]:
|
| 96 |
+
conv_out = conv_out[..., :Bx.shape[-1]]
|
| 97 |
+
elif conv_out.shape[-1] < Bx.shape[-1]:
|
| 98 |
+
conv_out = F.pad(conv_out, (0, Bx.shape[-1] - conv_out.shape[-1]))
|
| 99 |
+
|
| 100 |
+
y = C * conv_out
|
| 101 |
+
y = y.transpose(-1, -2).contiguous()
|
| 102 |
+
return self.out_proj(y)
|
| 103 |
+
|
| 104 |
+
|
| 105 |
+
def _shortconv_forward(self, *args, **kwargs):
|
| 106 |
+
return self.slow_forward(*args, **kwargs)
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
_PATCHED = False
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
def _install_patches() -> None:
|
| 113 |
+
global _PATCHED
|
| 114 |
+
if _PATCHED:
|
| 115 |
+
return
|
| 116 |
+
_lfm2_mod.create_causal_mask = _bidirectional_mask
|
| 117 |
+
Lfm2ShortConv.slow_forward = _noncausal_shortconv_forward
|
| 118 |
+
Lfm2ShortConv.forward = _shortconv_forward
|
| 119 |
+
_PATCHED = True
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
_install_patches()
|
| 123 |
+
|
| 124 |
+
|
| 125 |
+
def _set_attention_noncausal(model) -> None:
|
| 126 |
+
for module in model.modules():
|
| 127 |
+
if isinstance(module, Lfm2Attention):
|
| 128 |
+
module.is_causal = False
|
| 129 |
+
|
| 130 |
+
|
| 131 |
+
class Lfm2BidirectionalModel(Lfm2Model):
|
| 132 |
+
"""LFM2 patched for encoder-style use:
|
| 133 |
+
full bidirectional attention + non-causal short-conv."""
|
| 134 |
+
|
| 135 |
+
def __init__(self, config):
|
| 136 |
+
_install_patches()
|
| 137 |
+
super().__init__(config)
|
| 138 |
+
_set_attention_noncausal(self)
|
| 139 |
+
|
| 140 |
+
|
| 141 |
+
class Lfm2BidirectionalForMaskedLM(Lfm2PreTrainedModel):
|
| 142 |
+
"""LFM2 bidirectional encoder with a tied masked-LM head."""
|
| 143 |
+
|
| 144 |
+
config_class = Lfm2Config
|
| 145 |
+
base_model_prefix = "lfm2"
|
| 146 |
+
_tied_weights_keys = {"lm_head.weight": "lfm2.embed_tokens.weight"}
|
| 147 |
+
|
| 148 |
+
def __init__(self, config: Lfm2Config):
|
| 149 |
+
_install_patches()
|
| 150 |
+
config = type(config).from_dict({**config.to_dict(), "use_cache": False})
|
| 151 |
+
super().__init__(config)
|
| 152 |
+
self.lfm2 = Lfm2BidirectionalModel(config)
|
| 153 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 154 |
+
self.post_init()
|
| 155 |
+
self.lm_head.weight = self.lfm2.embed_tokens.weight
|
| 156 |
+
|
| 157 |
+
def get_input_embeddings(self):
|
| 158 |
+
return self.lfm2.embed_tokens
|
| 159 |
+
|
| 160 |
+
def set_input_embeddings(self, value):
|
| 161 |
+
self.lfm2.embed_tokens = value
|
| 162 |
+
|
| 163 |
+
def get_output_embeddings(self):
|
| 164 |
+
return self.lm_head
|
| 165 |
+
|
| 166 |
+
def set_output_embeddings(self, new_embeddings):
|
| 167 |
+
self.lm_head = new_embeddings
|
| 168 |
+
|
| 169 |
+
def forward(
|
| 170 |
+
self,
|
| 171 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 172 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 173 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 174 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 175 |
+
labels: Optional[torch.LongTensor] = None,
|
| 176 |
+
output_hidden_states: Optional[bool] = None,
|
| 177 |
+
output_attentions: Optional[bool] = None,
|
| 178 |
+
return_dict: Optional[bool] = None,
|
| 179 |
+
**kwargs,
|
| 180 |
+
) -> MaskedLMOutput:
|
| 181 |
+
return_dict = True if return_dict is None else return_dict
|
| 182 |
+
outputs = self.lfm2(
|
| 183 |
+
input_ids=input_ids,
|
| 184 |
+
attention_mask=attention_mask,
|
| 185 |
+
position_ids=position_ids,
|
| 186 |
+
inputs_embeds=inputs_embeds,
|
| 187 |
+
use_cache=False,
|
| 188 |
+
output_attentions=output_attentions,
|
| 189 |
+
output_hidden_states=output_hidden_states,
|
| 190 |
+
return_dict=True,
|
| 191 |
+
)
|
| 192 |
+
hidden = outputs.last_hidden_state
|
| 193 |
+
logits = self.lm_head(hidden)
|
| 194 |
+
|
| 195 |
+
loss = None
|
| 196 |
+
if labels is not None:
|
| 197 |
+
loss = F.cross_entropy(
|
| 198 |
+
logits.view(-1, self.config.vocab_size),
|
| 199 |
+
labels.view(-1),
|
| 200 |
+
ignore_index=-100,
|
| 201 |
+
)
|
| 202 |
+
|
| 203 |
+
if not return_dict:
|
| 204 |
+
out = (logits,) + outputs[1:]
|
| 205 |
+
return ((loss,) + out) if loss is not None else out
|
| 206 |
+
return MaskedLMOutput(
|
| 207 |
+
loss=loss,
|
| 208 |
+
logits=logits,
|
| 209 |
+
hidden_states=outputs.hidden_states,
|
| 210 |
+
attentions=outputs.attentions,
|
| 211 |
+
)
|
modules.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"idx": 0,
|
| 4 |
+
"name": "0",
|
| 5 |
+
"path": "",
|
| 6 |
+
"type": "sentence_transformers.models.Transformer"
|
| 7 |
+
},
|
| 8 |
+
{
|
| 9 |
+
"idx": 1,
|
| 10 |
+
"name": "1",
|
| 11 |
+
"path": "1_Pooling",
|
| 12 |
+
"type": "sentence_transformers.models.Pooling"
|
| 13 |
+
}
|
| 14 |
+
]
|
quantization_report.json
ADDED
|
@@ -0,0 +1,1414 @@
|
|
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|
| 1 |
+
{
|
| 2 |
+
"algorithm": "activation_calibrated_block_gptq_mxfp8_cuda",
|
| 3 |
+
"scope": "exact 32x32 Hessian and GPTQ error propagation within each MXFP8 group",
|
| 4 |
+
"source_model": "/home/ubuntu/artifacts/LFM2.5-Encoder-350M-Code-BF16",
|
| 5 |
+
"source_revision": null,
|
| 6 |
+
"corpus_sha256": "ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9",
|
| 7 |
+
"calibration": {
|
| 8 |
+
"samples": 256,
|
| 9 |
+
"sequence_length": 512,
|
| 10 |
+
"tokens": 131072,
|
| 11 |
+
"tokens_sha256": "b70b7dbcca44bd4febb6db72971ad810eb47f3bf841e468dc22b38a6ba341526",
|
| 12 |
+
"damp": 0.01,
|
| 13 |
+
"scale_offsets": [
|
| 14 |
+
0,
|
| 15 |
+
-1,
|
| 16 |
+
-2,
|
| 17 |
+
-3,
|
| 18 |
+
-4,
|
| 19 |
+
1,
|
| 20 |
+
2
|
| 21 |
+
]
|
| 22 |
+
},
|
| 23 |
+
"weighted_error_rtn": 401032315.790329,
|
| 24 |
+
"weighted_error_calibrated": 26127975.15027237,
|
| 25 |
+
"elapsed_seconds": 18.632522121000022,
|
| 26 |
+
"cuda": {
|
| 27 |
+
"device": "NVIDIA RTX A6000",
|
| 28 |
+
"torch": "2.13.0+cu130",
|
| 29 |
+
"peak_memory_bytes": 878556672
|
| 30 |
+
},
|
| 31 |
+
"layers": [
|
| 32 |
+
{
|
| 33 |
+
"path": "layers.0.conv.in_proj",
|
| 34 |
+
"shape": [
|
| 35 |
+
3072,
|
| 36 |
+
1024
|
| 37 |
+
],
|
| 38 |
+
"calibration_tokens": 131072,
|
| 39 |
+
"layer_index": 1,
|
| 40 |
+
"layer_count": 92,
|
| 41 |
+
"elapsed_seconds": 0.40941832699996894,
|
| 42 |
+
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tokenizer.json
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tokenizer_config.json
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