Source: https://ailoot.ai/models/en/liquid-lfm-2-5-embedding-350m-u003a-free/ Canonical: https://ailoot.ai/models/en/liquid-lfm-2-5-embedding-350m-u003a-free/ Language: en # LiquidAI: LFM2.5-Embedding-350M (free) [Markdown](https://ailoot.ai/models/en/liquid-lfm-2-5-embedding-350m-u003a-free/index.md) · [JSON](https://ailoot.ai/models/en/liquid-lfm-2-5-embedding-350m-u003a-free/facts.json) ## [LiquidAI: LFM2.5-Embedding-350M (free)](https://ailoot.ai/models/en/liquid-lfm-2-5-embedding-350m-u003a-free/) LiquidAI LFM2.5-Embedding-350M produces one 1024-dimensional vector per text for multilingual and cross-language retrieval. It targets 11 languages and short, 512-token inputs, with distinct query/document prefixes. - [**Ailoot Ultra · Embeddings** `POST /v1/embeddings` `liquid/lfm-2.5-embedding-350m:free`](https://ailoot.ai/models/en/liquid-lfm-2-5-embedding-350m-u003a-free/#method-0) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. LFM2.5-Embedding-350M is LiquidAI’s dense bi-encoder for multilingual and cross-language retrieval. It produces one 1024-dimensional vector for each text and targets short-context search across eleven languages. The named 350M model family uses a bidirectional adaptation of LFM2.5-350M-Base; the detailed model table gives approximately 354 million parameters for this embedding edition. #### A bidirectional retrieval representation The manufacturer adapts the backbone so attention and local convolutions can use context on both sides of a token. CLS-style pooling turns the processed text into one dense vector. Its model table describes seventeen layers: ten convolution blocks, six attention blocks and a pooling stage. This is a retrieval encoder, with its own preparation and output, rather than the text-generation task of the base model. The separately named LFM2.5-ColBERT-350M retains per-token vectors for late interaction. That representation requires a different index and comparison method. The Embedding model here supplies one vector per document or query, with cosine similarity as the documented comparison. #### Use the correct asymmetric prompts Prepare search queries with “query:” and source passages with “document:”. The manufacturer stores these prompts in its configuration and its reference workflow applies the matching prompt name. It specifically recommends using the corresponding role on each side because the model was trained with these prefixes. This document prefix is different from the “passage:” convention of E5 models. A practical product-search collection can hold descriptions in the supported languages. Prepare the user’s search wording as a query and each product description as a document. Store the original text, product identifier and language with the vector so a search result can return the corresponding record. For a support collection, retain the question’s relevant policy or instruction instead of replacing it with an unrelated short label. #### Languages and passage size The documented language set is English, Spanish, German, French, Italian, Portuguese, Arabic, Swedish, Norwegian, Japanese and Korean. The manufacturer explicitly targets both retrieval within a language and retrieval across language boundaries. Prepare real questions from the intended audience and inspect the returned document text in those languages. The document-length specification is 512 tokens. Divide a long source into coherent passages which preserve the heading, definitions and conditions needed by a retrieved result. The role prefix and added context belong to the prepared input. A FAQ answer, a product explanation or a focused section of a technical document is a more suitable unit than a whole oversized manual. #### Training and matching The maker describes three training stages: English contrastive pretraining, multilingual and cross-language distillation, then fine-tuning with hard-mined negatives. The resulting model is intended for product catalogues, FAQ and support collections, file or note search, and enterprise knowledge retrieval. Those are tasks for the text representations, not claims of a particular application’s deployment. The producer’s example normalises query and document vectors and ranks them with a dot product, equivalent to cosine similarity. Precompute the corpus representations when the source material is prepared, then embed a search query and return candidate documents with their text and identifiers. Keep the selected model and role convention with the index so the numerical comparison uses compatible representations. The useful result is a compact dense search index or a query vector that retrieves source material across the documented language set. This exact LFM2.5 Embedding edition remains separate from its ColBERT companion, older LFM2 retrievers and externally quantised implementations. The selected passage text is the evidence used by a reader or a later RAG answer stage. **Ailoot Ultra · Embeddings** `POST /v1/embeddings` `liquid/lfm-2.5-embedding-350m:free` [`liquid/lfm-2.5-embedding-350m:free`](https://ailoot.ai/models/en/#m=liquid%2Flfm-2.5-embedding-350m%3Afree&route=Ultra&caller=liquid%2Flfm-2.5-embedding-350m%3Afree&fmt=embeddings) LiquidAI /`liquid/lfm-2.5-embedding-350m:free` ### Overview LiquidAI LFM2.5-Embedding-350M produces one 1024-dimensional vector per text for multilingual and cross-language retrieval. It targets 11 languages and short, 512-token inputs, with distinct query/document prefixes. Accepted inputs Text · token\_ids Results embedding ### Available in Ailoot · inputs & methods **Ailoot Ultra · Embeddings** `POST /v1/embeddings` Input types `text`, `token_ids` Output types `embedding` Context 512 tokens Output format json | Field | Type | Requirement | Values & limits | Default | | --- | --- | --- | --- | --- | | Field`model` | Type`string` | RequirementRequired | Values & limits liquid/lfm-2.5-embedding-350m:free | Default— | | Field`input` | Type`string\|string[]\|integer[]\|integer[][]` | RequirementRequired | Values & limits Minimum items: 1 Conditions: Nonempty homogeneous batch. Token IDs are nonnegative integers. Input budget: Maximum: 512 · Text unit: UTF8 bytes · Token unit: token-array elements | Default— | | Field`encoding_format` | Type`string` | RequirementOptional | Values & limits float | Defaultfloat | | Field`dimensions` | Type`integer` | RequirementNot supported | Values & limits Minimum: 1 | Default— | | Field`user` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— | | Field`session_id` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— | | Field`trace` | Type`json_value` | RequirementOptional | Values & limits Conditions: Source allows this JSON metadata field; exact upstream semantics are model-specific. | Default— | | Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options rejected | Default— | Input material combinations **text\_to\_embedding** Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, input | | Parameter`input_shapes` | Contractstring, string\[\], nonnegative\_integer\_token\_array, nonnegative\_integer\_token\_array\[\] | | Parameter`encoding_format` | Contractfloat | | Parameter`dimensions_override` | Contractfalse | | Parameter`max_input_budget_units` | Contract512 | | Parameter`multimodal_objects` | Contractfalse | | Parameter`material_types` | Contracttext, token\_ids | Lifecycle: `synchronous_json` Keep every vector index and dimension. Multimodal model name does not admit image objects on this exact path. ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Standard rate When fast mode is selected Flex mode Standard rate When flex mode is selected Maximum reasoning effort Standard rate When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Embeddings curlPython SDK ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" python3 - <<'PY_REQUEST' import json,os from pathlib import Path p=json.loads('{"model": "liquid/lfm-2.5-embedding-350m:free", "input": ["First document", "Second document"]}') Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/embeddings \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path r=json.loads(Path("result.json").read_text()) for x in r.get("data",[]): print(json.dumps({"index":x.get("index"),"embedding":x.get("embedding")})) PY_RESULT ``` ```python from openai import OpenAI import os client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) result=client.embeddings.create(model='liquid/lfm-2.5-embedding-350m:free',input=["First document","Second document"]) print(result) for item in result.data:print(item.index,item.embedding) ``` ### Receive and save results #### Ailoot Ultra · Embeddings json ##### Keep all completed results Keep every complete returned response, file and content part. Keep every vector with its index. Vector count must equal input count; dimensions must be consistent. ##### If the original response needs checking No embedding job endpoint is introduced. Response fields | Field | Type | | --- | --- | | Field`object` | Typestring | | Field`model` | Typestring | | Field`data[].index` | Typeinteger | | Field`data[].embedding` | Typefinite float\[\] | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. | ### Aliases #### Model aliases **Ailoot Ultra** [`liquid/lfm-2.5-embedding-350m:free`](https://ailoot.ai/models/en/#m=liquid%2Flfm-2.5-embedding-350m%3Afree&route=Ultra&caller=liquid%2Flfm-2.5-embedding-350m%3Afree&fmt=embeddings) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`liquid/lfm-2.5-embedding-350m:free`](https://ailoot.ai/models/en/#m=liquid%2Flfm-2.5-embedding-350m%3Afree&route=Ultra&caller=liquid%2Flfm-2.5-embedding-350m%3Afree&fmt=embeddings) FAQ · LiquidAI: LFM2.5-Embedding-350M (free) What inputs and results are available? `POST /v1/embeddings` · `liquid/lfm-2.5-embedding-350m:free`: `text`, `token_ids` → `embedding` Which fields does the request use? `POST /v1/embeddings` · `liquid/lfm-2.5-embedding-350m:free`: `model`, `input`, `encoding_format`, `provider` What limits apply? `POST /v1/embeddings` · `liquid/lfm-2.5-embedding-350m:free`: `ctx`: `512`; `input.budget.maximum`: `512` (UTF8 bytes); `input.min_items`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. 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