gte-Qwen2-1.5B-instruct-Q8_0

This version

This model was converted to a 8-bit GGUF format (q8_0) from Alibaba-NLP/gte-Qwen2-1.5B-instruct using llama-quantize built from llama.cpp.

Custom conversion script settings:

  "gte-Qwen2-1.5B-instruct": {
    "model_name": "gte-Qwen2-1.5B-instruct", 
    "hq_quant_type": "f32",
    "final_quant_type": "q8_0",
    "produce_final_quant": true,
    "parts_num": 2,
    "max_shard_size_gb": 4,
    "numexpr_max_thread": 8
    }

Please refer to the original model card for more details on the unquantized model, including its metrics, which may be different (typically slightly worse) for this quantized version.

gte-Qwen2-1.5B-instruct

gte-Qwen2-1.5B-instruct is the latest model in the gte (General Text Embedding) model family. The model is built on Qwen2-1.5B LLM model and use the same training data and strategies as the gte-Qwen2-7B-instruct model.

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

• Model Type: GTE (General Text Embeddings)
• Model Size: 1.5B
• Embedding Dimension: 1536
• 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]

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-Qwen2-1.5B-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              = 1.5B
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              = 1536
llama_model_loader: - kv  11:                  qwen2.feed_forward_length u32              = 8960
llama_model_loader: - kv  12:                 qwen2.attention.head_count u32              = 12
llama_model_loader: - kv  13:              qwen2.attention.head_count_kv u32              = 2
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              = 7
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              = 2
llama_model_loader: - kv  30:                        split.tensors.count i32              = 339
llama_model_loader: - type  f32:  141 tensors
llama_model_loader: - type q8_0:  198 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           = 1536
llm_load_print_meta: n_layer          = 28
llm_load_print_meta: n_head           = 12
llm_load_print_meta: n_head_kv        = 2
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            = 6
llm_load_print_meta: n_embd_k_gqa     = 256
llm_load_print_meta: n_embd_v_gqa     = 256
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             = 8960
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       = 1.5B
llm_load_print_meta: model ftype      = Q8_0
llm_load_print_meta: model params     = 1.78 B
llm_load_print_meta: model size       = 1.76 GiB (8.50 BPW) 
llm_load_print_meta: general.name     = gte-Qwen2-1.5B-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 =  1008.90 MiB
llm_load_tensors:   CPU_Mapped model buffer size =   791.29 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 =  3584.00 MiB
llama_new_context_with_model: KV self size  = 3584.00 MiB, K (f16): 1792.00 MiB, V (f16): 1792.00 MiB
llama_new_context_with_model:        CPU  output buffer size =     0.01 MiB
llama_new_context_with_model:        CPU compute buffer size =  3340.01 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 or install llama-server binary from llama.cpp, preferably with GPU support.

CLI

Server

# using remote HF repo address (with model file(s) to be downloaded and cached locally)
$ llama-server --hf-repo mirekphd/gte-Qwen2-1.5B-instruct-Q8_0 --hf-file gte-Qwen2-1.5B-instruct-Q8_0-00001-of-00002.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-Qwen2-1.5B-instruct-Q8_0/gte-Qwen2-1.5B-instruct-Q8_0-00001-of-00002.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}
}