Update README.md

README.md

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+GGUF Quants of [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) created using [llama.cpp](https://github.com/ggerganov/llama.cpp)
+
+Original model card:
+***
+
+<h1 align="center">Snowflake's Arctic-embed-l-v2.0</h1>
+<h4 align="center">
+   <p>
+       <a href=#news>News</a> |
+       <a href=#models>Models</a> |
+       <a href=#usage>Usage</a>  |
+       <a href="#evaluation">Evaluation</a> |
+       <a href="#contact">Contact</a> |
+       <a href="#faq">FAQ</a>
+       <a href="#license">License</a> |
+       <a href="#acknowledgement">Acknowledgement</a>
+   <p>
+</h4>
+
+<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=18f5b1a3-da66-4f25-92d3-21da829509c3" />
+
+## News
+- 12/11/2024: Release of [Technical Report](https://arxiv.org/abs/2412.04506)
+- 12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind.
+
+## Models
+Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency. 
+Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English. 
+Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale.
+
+Key Features:
+
+1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
+
+2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale. 
+
+3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training.
+
+4. Drop-In Replacement: arctic-embed-l-v2.0 builds on BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which allows direct drop-in inference replacement with any form of new libraries, kernels, inference engines etc. 
+
+5. Long Context Support: arctic-embed-l-v2.0 builds on [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) which can support a context window of up to 8192 via the use of RoPE. 
+
+
+### Quality Benchmarks
+Unlike most other open-source models, Arctic-embed-l-v2.0 excels across English (via MTEB Retrieval) and multilingual (via MIRACL and CLEF). 
+You no longer need to support models to empower high-quality English and multilingual retrieval. All numbers mentioned below are the average NDCG@10 across the dataset being discussed. 
+
+| Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) |
+|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| **snowflake-arctic-l-v2.0** | 568M | 303M | 1024 | **55.6** | 55.8 | **52.9** | **54.3** |
+| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
+| snowflake-arctic-l | 335M | 303M | 1024 | 56.0 | 34.8 | 38.2 | 33.7 |
+| me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 |
+| bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | **56.8** | 40.8 | 41.3 |
+| gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 |
+
+Aside from high-quality retrieval arctic delivers embeddings that are easily compressible. Leverage vector truncation via MRL to decrease vector size by 4x with less than 3% degredation in quality. 
+Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc. 
+
+| Model |  | BEIR (15) | Relative Performance | MIRACL (4) | Relative Performance | CLEF (5) | Relative Performance | CLEF (Full) | Relative Performance |
+|---|---|:---:|:---:|:---:|:---:|:---:|---|---|---|
+| snowflake-arctic-l-v2.0 | 1024 | 55.6 | N/A | 55.8 | N/A | 52.9 | N/A | 54.3 | N/A |
+| snowflake-arctic-l-v2.0 | 256 | 54.3 | -0.18% | 54.3 | -2.70% | 51.9 | -1.81% | 53.4 | -1.53% |
+
+## Usage
+
+### Using Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
+model = SentenceTransformer(model_name)
+
+# Define the queries and documents
+queries = ['what is snowflake?', 'Where can I get the best tacos?']
+documents = ['The Data Cloud!', 'Mexico City of Course!']
+
+# Compute embeddings: use `prompt_name="query"` to encode queries!
+query_embeddings = model.encode(queries, prompt_name="query") 
+document_embeddings = model.encode(documents)
+
+# Compute cosine similarity scores
+scores = model.similarity(query_embeddings, document_embeddings)
+
+# Output the results
+for query, query_scores in zip(queries, scores):
+    doc_score_pairs = list(zip(documents, query_scores))
+    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+    print("Query:", query)
+    for document, score in doc_score_pairs:
+        print(score, document)
+
+```
+
+
+
+### Using Huggingface Transformers
+
+
+You can use the transformers package to use Snowflake's arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name, add_pooling_layer=False)
+model.eval()
+
+query_prefix = 'query: '
+queries  = ['what is snowflake?', 'Where can I get the best tacos?']
+queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
+query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=8192)
+
+documents = ['The Data Cloud!', 'Mexico City of Course!']
+document_tokens =  tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=8192)
+
+# Compute token embeddings
+with torch.no_grad():
+    query_embeddings = model(**query_tokens)[0][:, 0]
+    document_embeddings = model(**document_tokens)[0][:, 0]
+
+
+# normalize embeddings
+query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
+document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
+
+scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
+for query, query_scores in zip(queries, scores):
+    doc_score_pairs = list(zip(documents, query_scores))
+    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+    #Output passages & scores
+    print("Query:", query)
+    for document, score in doc_score_pairs:
+        print(score, document)
+```
+
+
+This should produce the following scores
+
+```
+Query: what is snowflake?
+tensor(0.2715) The Data Cloud!
+tensor(0.0661) Mexico City of Course!
+Query: Where can I get the best tacos?
+tensor(0.2797) Mexico City of Course!
+tensor(0.1250) The Data Cloud!
+```
+
+### Using Huggingface Transformers.js
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+You can then use the model for retrieval, as follows:
+
+```js
+import { pipeline, dot } from '@huggingface/transformers';
+
+// Create feature extraction pipeline
+const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0', {
+    dtype: 'q8',
+});
+
+// Generate sentence embeddings
+const sentences = [
+    'query: what is snowflake?',
+    'The Data Cloud!',
+    'Mexico City of Course!',
+]
+const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
+
+// Compute similarity scores
+const [source_embeddings, ...document_embeddings ] = output.tolist();
+const similarities = document_embeddings.map(x => dot(source_embeddings, x));
+console.log(similarities); // [0.24783534471401417, 0.05313122704326892]
+```
+
+
+## Contact
+
+
+Feel free to open an issue or pull request if you have any questions or suggestions about this project.
+You also can email Daniel Campos([email protected]).
+
+
+## License
+Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.