Update README.md

README.md

@@ -1,3 +1,171 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- zh
+- ru
+- es
+- fr
+- de
+- ar
+- nl
+- vi
+- hi
+- ko
+- ja
+- it
+- id
+- pt
+- pl
+- tr
+- da
+- th
+- sv
+- fa
+- uk
+- cs
+- 'no'
+- el
+- ca
+- ro
+- fi
+- bg
+- tl
+- gl
+- my
+- hy
+- km
+- ne
+- hu
+- eu
+- he
+- lo
+- sw
+- az
+- lv
+- si
+- sk
+- tg
+- et
+- lt
+- ms
+- hr
+- is
+- sl
+- sr
+- ur
+- bn
+- af
+- ta
+- ka
+- te
+- ml
+- mn
+- nn
+- kk
+- cy
+- mr
+- sq
+- nb
+- mk
+- jv
+- kn
+- eo
+- la
+- gu
+- uz
+- am
+- oc
+- be
+- mg
+- vo
+- pa
+- lb
+- ht
+- br
+- ga
+- xh
+- tt
+- bs
+- yo
+base_model:
+- codefuse-ai/F2LLM-v2-4B-Preview
+pipeline_tag: feature-extraction
+library_name: transformers
+tags:
+- sentence-transformers
+datasets:
+- codefuse-ai/F2LLM-v2
+---
+
+# F2LLM-v2-4B
+
+F2LLM-v2 is a family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a curated composite of 60 million publicly available high-quality data, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages.
+
+## Usage
+
+### With Sentence Transformers
+
+To encode text with the [Sentence Transformers](https://www.sbert.net/) library:
+
+```python
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer("codefuse-ai/F2LLM-v2-4B", device="cuda:0", model_kwargs={"torch_dtype": "bfloat16"})
+# Some sample query and documents
+query = "What is F2LLM used for?"
+documents = [
+    'We present F2LLM, a family of fully open embedding LLMs that achieve a strong balance between model size, training data, and embedding performance.',
+    'F2LLM is a model for computing text embeddings that can be used for various NLP tasks such as information retrieval, semantic search, and text classification.',
+    'F2LLM 是 CodeFuse 开源的系列嵌入模型。',
+    'F2LLM — это модель вычисления встраивания текста, которую можно использовать для различных задач НЛП, таких как поиск информации, семантический поиск и классификация текста.'
+]
+# Encode the query and documents separately. The encode_query method uses the query prompt
+query_embedding = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+print(query_embedding.shape, document_embeddings.shape)
+# (2560,) (4, 2560)
+# Compute cosine similarity between the query and documents
+similarity = model.similarity(query_embedding, document_embeddings)
+print(similarity)
+# tensor([[0.6348, 0.8547, 0.7168, 0.8356]])
+```
+
+### With Transformers
+
+Or directly with the [Transformers](https://huggingface.co/docs/transformers/index) library:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch
+import torch.nn.functional as F
+model_path = "codefuse-ai/F2LLM-v2-4B"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map={'': 0})
+query = "What is F2LLM used for?"
+query_prompt = "Instruct: Given a question, retrieve passages that can help answer the question.\nQuery: "
+documents = [
+    'We present F2LLM, a family of fully open embedding LLMs that achieve a strong balance between model size, training data, and embedding performance.',
+    'F2LLM is a model for computing text embeddings that can be used for various NLP tasks such as information retrieval, semantic search, and text classification.',
+    'F2LLM 是 CodeFuse 开源的系列嵌入模型。',
+    'F2LLM — это модель вычисления встраивания текста, которую можно использовать для различных задач НЛП, таких как поиск информации, семантический поиск и классификация текста.'
+]
+def encode(sentences):
+    batch_size = len(sentences)
+    # the tokenizer will automatically add eos token
+    tokenized_inputs = tokenizer(sentences, padding=True, return_tensors='pt').to(model.device)
+    last_hidden_state = model(**tokenized_inputs).last_hidden_state
+    eos_positions = tokenized_inputs.attention_mask.sum(dim=1) - 1
+    embeddings = last_hidden_state[torch.arange(batch_size, device=model.device), eos_positions]
+    embeddings = F.normalize(embeddings, p=2, dim=1)
+    return embeddings
+# Encode the query and documents
+query_embedding = encode([query_prompt + query])
+document_embeddings = encode(documents)
+print(query_embedding.shape, document_embeddings.shape)
+# torch.Size([1, 2560]) torch.Size([4, 2560])
+# Compute cosine similarity between the query and documents
+similarity = query_embedding @ document_embeddings.T
+print(similarity)
+# tensor([[0.6328, 0.8555, 0.7148, 0.8398]], device='cuda:0',
+#        dtype=torch.bfloat16, grad_fn=<MmBackward0>)
+```