初始化项目，由ModelHub XC社区提供模型

Model: nixiesearch/nixie-querygen-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+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:
+
+![datasets](datasets.png)
+
+## 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

config.json

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+{
+  "_name_or_path": "mistralai/Mistral-7B-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
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+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.36.2"
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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