File size: 4,650 Bytes
111dd00 6bd6c7e 111dd00 c7377d8 51e9929 c7377d8 f849654 c7377d8 f849654 c7377d8 4fbbd61 6335399 c7377d8 4fbbd61 c7377d8 |
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 |
---
license: apache-2.0
datasets:
- BeIR/nq
- embedding-data/PAQ_pairs
- sentence-transformers/msmarco-hard-negatives
- leminda-ai/s2orc_small
- lucadiliello/triviaqa
- pietrolesci/agnews
- mteb/amazon_reviews_multi
- multiIR/ccnews2016-8multi
- eli5
- gooaq
- quora
- lucadiliello/searchqa
- flax-sentence-embeddings/stackexchange_math_jsonl
- yahoo_answers_qa
- EdinburghNLP/xsum
- wikihow
- rajpurkar/squad_v2
- nixiesearch/amazon-esci
- osunlp/Mind2Web
- derek-thomas/dataset-creator-askreddit
language:
- en
---
# nixie-querygen-v2
A [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) fine-tuned on query generation task. Main use cases:
* synthetic query generation for downstream embedding fine-tuning tasks - when you have only documents and no queries/labels. Such task can be done with the [nixietune](https://github.com/nixiesearch/nixietune) toolkit, see the `nixietune.qgen.generate` recipe.
* synthetic dataset expansion for further embedding training - when you DO have query-document pairs, but only a few. You can fine-tune the `nixie-querygen-v2` on existing pairs, and then expand your document corpus with synthetic queries (which are still based on your few real ones). See `nixietune.qgen.train` recipe.
The idea behind the approach is taken from the [doqT5query](https://github.com/castorini/docTTTTTquery) model. See the original paper [Rodrigo Nogueira and Jimmy Lin. From doc2query to docTTTTTquery.](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)
## Training data
We used [200k query-document pairs](https://huggingface.co/datasets/nixiesearch/query-positive-pairs-small) sampled randomly from a diverse set of IR datasets:
## Flavours
This repo has multiple versions of the model:
* model-*.safetensors: Pytorch FP16 checkpoint, suitable for down-stream fine-tuning
* ggml-model-f16.gguf: GGUF F16 non-quantized [llama-cpp](https://github.com/ggerganov/llama.cpp) checkpoint, for CPU inference
* ggml-model-q4.gguf: GGUF Q4_0 quantized [llama-cpp](https://github.com/ggerganov/llama.cpp) checkpoint, for fast (and less precise) CPU inference.
## Prompt formats
The model accepts the followinng prompt format:
```
<document next> [short|medium|long]? [question|regular]? query:
```
Some notes on format:
* `[short|medium|long]` and `[question|regular]` fragments are optional and can be skipped.
* the prompt suffix `query:` has no trailing space, be careful.
## Inference example
With [llama-cpp](https://github.com/ggerganov/llama.cpp) and Q4 model the inference can be done on a CPU:
```bash
$ ./main -m ~/models/nixie-querygen-v2/ggml-model-q4.gguf -p "git lfs track will \
begin tracking a new file or an existing file that is already checked in to your \
repository. When you run git lfs track and then commit that change, it will \
update the file, replacing it with the LFS pointer contents. short regular query:" -s 1
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampling order:
CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temp
generate: n_ctx = 512, n_batch = 512, n_predict = -1, n_keep = 0
git lfs track will begin tracking a new file or an existing file that is
already checked in to your repository. When you run git lfs track and then
commit that change, it will update the file, replacing it with the LFS
pointer contents. short regular query: git-lfs track [end of text]
```
## Training config
The model is trained with the follwing [nixietune](https://github.com/nixiesearch/nixietune) config:
```json
{
"train_dataset": "/home/shutty/data/nixiesearch-datasets/query-doc/data/train",
"eval_dataset": "/home/shutty/data/nixiesearch-datasets/query-doc/data/test",
"seq_len": 512,
"model_name_or_path": "mistralai/Mistral-7B-v0.1",
"output_dir": "mistral-qgen",
"num_train_epochs": 1,
"seed": 33,
"per_device_train_batch_size": 6,
"per_device_eval_batch_size": 2,
"bf16": true,
"logging_dir": "logs",
"gradient_checkpointing": true,
"gradient_accumulation_steps": 1,
"dataloader_num_workers": 14,
"eval_steps": 0.03,
"logging_steps": 0.03,
"evaluation_strategy": "steps",
"torch_compile": false,
"report_to": [],
"save_strategy": "epoch",
"streaming": false,
"do_eval": true,
"label_names": [
"labels"
]
}
```
## License
Apache 2.0 |