Added gte-Qwen2-7B-instruct-F32 GGUF model files.

.gitattributes

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README.md

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+---
+tags:
+- mteb
+- sentence-transformers
+- transformers
+- Qwen2
+- sentence-similarity
+- llama-cpp
+license: apache-2.0
+---
+## This version
+
+This model was converted from the 32-bit original safetensors format to a (lossless in this case) **32-bit GGUF format (`f32`)** from **[`Alibaba-NLP/gte-Qwen2-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct)** using `llama-quantize` built from [`llama.cpp`](https://github.com/ggerganov/llama.cpp).
+
+Custom conversion script settings:
+```json
+"gte-Qwen2-7B-instruct": {
+    "model_name": "gte-Qwen2-7B-instruct", 
+    "hq_quant_type": "f32",
+    "final_quant_type": "",
+    "produce_final_quant": false,
+    "parts_num": 4,
+    "max_shard_size_gb": 4,
+    "numexpr_max_thread": 8
+    }
+```
+Please refer to the [original model card](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) for more details on the unquantized model, including its metrics, which may be different (typically slightly worse) for this quantized version.
+
+
+## gte-Qwen2-7B-instruct
+
+**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024).
+
+Recently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models.
+
+The model incorporates several key advancements:
+
+- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
+- Instruction tuning, applied solely on the query side for streamlined efficiency
+- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
+
+
+## Model Information
+
+### Overview
+- Model Type: GTE (General Text Embeddings) 
+- Model Size: 7B
+- Embedding Dimension: 3584
+- Context Window: 131072
+### Supported languages
+- North America: English
+- Western Europe: German, French, Spanish, Portuguese, Italian, Dutch
+- Eastern & Central Europe: Russian, Czech, Polish
+- Middle East: Arabic, Persian, Hebrew, Turkish
+- Eastern Asia: Chinese, Japanese, Korean
+- South-Eastern Asia: Vietnamese, Thai, Indonesian, Malay, Lao, Burmese, Cebuano, Khmer, Tagalog
+- Southern Asia: Hindi, Bengali, Urdu 
+- [[source](https://qwenlm.github.io/blog/qwen2/)]
+### Details
+```
+llama_model_loader: - kv   0:                       general.architecture str              = qwen2
+llama_model_loader: - kv   1:                               general.type str              = model
+llama_model_loader: - kv   2:                               general.name str              = gte-Qwen2-7B-instruct
+llama_model_loader: - kv   3:                           general.finetune str              = instruct
+llama_model_loader: - kv   4:                           general.basename str              = gte-Qwen2
+llama_model_loader: - kv   5:                         general.size_label str              = 7B
+llama_model_loader: - kv   6:                            general.license str              = apache-2.0
+llama_model_loader: - kv   7:                               general.tags arr[str,5]       = ["mteb", "sentence-transformers", "tr...
+llama_model_loader: - kv   8:                          qwen2.block_count u32              = 28
+llama_model_loader: - kv   9:                       qwen2.context_length u32              = 131072
+llama_model_loader: - kv  10:                     qwen2.embedding_length u32              = 3584
+llama_model_loader: - kv  11:                  qwen2.feed_forward_length u32              = 18944
+llama_model_loader: - kv  12:                 qwen2.attention.head_count u32              = 28
+llama_model_loader: - kv  13:              qwen2.attention.head_count_kv u32              = 4
+llama_model_loader: - kv  14:                       qwen2.rope.freq_base f32              = 1000000.000000
+llama_model_loader: - kv  15:     qwen2.attention.layer_norm_rms_epsilon f32              = 0.000001
+llama_model_loader: - kv  16:                          general.file_type u32              = 0
+llama_model_loader: - kv  17:                       tokenizer.ggml.model str              = gpt2
+llama_model_loader: - kv  18:                         tokenizer.ggml.pre str              = qwen2
+llama_model_loader: - kv  19:                      tokenizer.ggml.tokens arr[str,151646]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+llama_model_loader: - kv  20:                  tokenizer.ggml.token_type arr[i32,151646]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+llama_model_loader: - kv  21:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+llama_model_loader: - kv  22:                tokenizer.ggml.eos_token_id u32              = 151643
+llama_model_loader: - kv  23:            tokenizer.ggml.padding_token_id u32              = 151643
+llama_model_loader: - kv  24:                tokenizer.ggml.bos_token_id u32              = 151643
+llama_model_loader: - kv  25:               tokenizer.ggml.add_eos_token bool             = true
+llama_model_loader: - kv  26:                    tokenizer.chat_template str              = {% for message in messages %}{{'<|im_...
+llama_model_loader: - kv  27:               general.quantization_version u32              = 2
+llama_model_loader: - kv  28:                                   split.no u16              = 0
+llama_model_loader: - kv  29:                                split.count u16              = 8
+llama_model_loader: - kv  30:                        split.tensors.count i32              = 339
+llama_model_loader: - type  f32:  339 tensors
+llm_load_vocab: special tokens cache size = 3
+llm_load_vocab: token to piece cache size = 0.9308 MB
+llm_load_print_meta: format           = GGUF V3 (latest)
+llm_load_print_meta: arch             = qwen2
+llm_load_print_meta: vocab type       = BPE
+llm_load_print_meta: n_vocab          = 151646
+llm_load_print_meta: n_merges         = 151387
+llm_load_print_meta: vocab_only       = 0
+llm_load_print_meta: n_ctx_train      = 131072
+llm_load_print_meta: n_embd           = 3584
+llm_load_print_meta: n_layer          = 28
+llm_load_print_meta: n_head           = 28
+llm_load_print_meta: n_head_kv        = 4
+llm_load_print_meta: n_rot            = 128
+llm_load_print_meta: n_swa            = 0
+llm_load_print_meta: n_embd_head_k    = 128
+llm_load_print_meta: n_embd_head_v    = 128
+llm_load_print_meta: n_gqa            = 7
+llm_load_print_meta: n_embd_k_gqa     = 512
+llm_load_print_meta: n_embd_v_gqa     = 512
+llm_load_print_meta: f_norm_eps       = 0.0e+00
+llm_load_print_meta: f_norm_rms_eps   = 1.0e-06
+llm_load_print_meta: f_clamp_kqv      = 0.0e+00
+llm_load_print_meta: f_max_alibi_bias = 0.0e+00
+llm_load_print_meta: f_logit_scale    = 0.0e+00
+llm_load_print_meta: n_ff             = 18944
+llm_load_print_meta: n_expert         = 0
+llm_load_print_meta: n_expert_used    = 0
+llm_load_print_meta: causal attn      = 1
+llm_load_print_meta: pooling type     = 0
+llm_load_print_meta: rope type        = 2
+llm_load_print_meta: rope scaling     = linear
+llm_load_print_meta: freq_base_train  = 1000000.0
+llm_load_print_meta: freq_scale_train = 1
+llm_load_print_meta: n_ctx_orig_yarn  = 131072
+llm_load_print_meta: rope_finetuned   = unknown
+llm_load_print_meta: ssm_d_conv       = 0
+llm_load_print_meta: ssm_d_inner      = 0
+llm_load_print_meta: ssm_d_state      = 0
+llm_load_print_meta: ssm_dt_rank      = 0
+llm_load_print_meta: ssm_dt_b_c_rms   = 0
+llm_load_print_meta: model type       = 7B
+llm_load_print_meta: model ftype      = all F32
+llm_load_print_meta: model params     = 7.61 B
+llm_load_print_meta: model size       = 28.36 GiB (32.00 BPW) 
+llm_load_print_meta: general.name     = gte-Qwen2-7B-instruct
+llm_load_print_meta: BOS token        = 151643 '<|endoftext|>'
+llm_load_print_meta: EOS token        = 151643 '<|endoftext|>'
+llm_load_print_meta: EOT token        = 151645 '<|im_end|>'
+llm_load_print_meta: PAD token        = 151643 '<|endoftext|>'
+llm_load_print_meta: LF token         = 148848 'ÄĬ'
+llm_load_print_meta: EOG token        = 151643 '<|endoftext|>'
+llm_load_print_meta: EOG token        = 151645 '<|im_end|>'
+llm_load_print_meta: max token length = 256
+llm_load_tensors:   CPU_Mapped model buffer size =  3795.37 MiB
+llm_load_tensors:   CPU_Mapped model buffer size =  3612.20 MiB
+llm_load_tensors:   CPU_Mapped model buffer size =  3668.20 MiB
+llm_load_tensors:   CPU_Mapped model buffer size =  3703.16 MiB
+llm_load_tensors:   CPU_Mapped model buffer size =  3556.17 MiB
+llm_load_tensors:   CPU_Mapped model buffer size =  3556.19 MiB
+llm_load_tensors:   CPU_Mapped model buffer size =  3556.18 MiB
+llm_load_tensors:   CPU_Mapped model buffer size =  3592.38 MiB
+........................................................................................
+llama_new_context_with_model: n_seq_max     = 1
+llama_new_context_with_model: n_ctx         = 131072
+llama_new_context_with_model: n_ctx_per_seq = 131072
+llama_new_context_with_model: n_batch       = 2048
+llama_new_context_with_model: n_ubatch      = 512
+llama_new_context_with_model: flash_attn    = 0
+llama_new_context_with_model: freq_base     = 1000000.0
+llama_new_context_with_model: freq_scale    = 1
+llama_kv_cache_init:        CPU KV buffer size =  7168.00 MiB
+llama_new_context_with_model: KV self size  = 7168.00 MiB, K (f16): 3584.00 MiB, V (f16): 3584.00 MiB
+llama_new_context_with_model:        CPU  output buffer size =     0.01 MiB
+llama_new_context_with_model:        CPU compute buffer size =  7452.01 MiB
+llama_new_context_with_model: graph nodes  = 986
+llama_new_context_with_model: graph splits = 1
+```
+
+## Usage 
+
+### Sentence Transformers
+
+### Transformers
+
+## Inference 
+
+### Using `llama.cpp` to get embeddings in CPU and/or GPU
+First [build](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md) or [install](https://github.com/ggerganov/llama.cpp/blob/master/docs/install.md) **`llama-server`** binary from [`llama.cpp`](https://github.com/ggerganov/llama.cpp), preferably with GPU support.
+### CLI
+### Server
+```bash
+# using remote HF repo address (with model file(s) to be downloaded and cached locally)
+$ llama-server --hf-repo mirekphd/gte-Qwen2-7B-instruct-F32 --hf-file gte-Qwen2-7B-instruct-F32-00001-of-00008.gguf --n-gpu-layers 0 --ctx-size 131072 --embeddings
+
+# using a previously downloaded local model file(s)
+$ llama-server --model <path-to-hf-models>/mirekphd/gte-Qwen2-7B-instruct-F32/gte-Qwen2-7B-instruct-F32-00001-of-00008.gguf --n-gpu-layers 0 --ctx-size 131072 --embeddings
+
+```
+
+## Evaluation
+
+### MTEB & C-MTEB
+
+## Cloud API Services
+
+## Citation
+If you find our paper or models helpful, please consider cite:
+
+```
+@article{li2023towards,
+  title={Towards general text embeddings with multi-stage contrastive learning},
+  author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
+  journal={arXiv preprint arXiv:2308.03281},
+  year={2023}
+}
+```

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