sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:156 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:Snowflake/snowflake-arctic-embed-l base_model:finetune:Snowflake/snowflake-arctic-embed-l model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Snowflake/snowflake-arctic-embed-l
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:156
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'content=''1. What are the names of the three best available models
    that were freely accessible for a few months this year?  \n2. In what year does
    the author expect the prompt-driven custom interface feature to be widely integrated
    into products?'' additional_kwargs={''refusal'': None} response_metadata={''token_usage'':
    {''completion_tokens'': 47, ''prompt_tokens'': 161, ''total_tokens'': 208, ''completion_tokens_details'':
    {''accepted_prediction_tokens'': 0, ''audio_tokens'': 0, ''reasoning_tokens'':
    0, ''rejected_prediction_tokens'': 0}, ''prompt_tokens_details'': {''audio_tokens'':
    0, ''cached_tokens'': 0}}, ''model_name'': ''gpt-4o-mini-2024-07-18'', ''system_fingerprint'':
    ''fp_13eed4fce1'', ''finish_reason'': ''stop'', ''logprobs'': None} id=''run-97f0e521-5a7a-4195-80c1-6bcbe1888a21-0''
    usage_metadata={''input_tokens'': 161, ''output_tokens'': 47, ''total_tokens'':
    208, ''input_token_details'': {''audio'': 0, ''cache_read'': 0}, ''output_token_details'':
    {''audio'': 0, ''reasoning'': 0}}'
  sentences:
  - 'Against this photo of butterflies at the California Academy of Sciences:



    A shallow dish, likely a hummingbird or butterfly feeder, is red.  Pieces of orange
    slices of fruit are visible inside the dish.

    Two butterflies are positioned in the feeder, one is a dark brown/black butterfly
    with white/cream-colored markings.  The other is a large, brown butterfly with
    patterns of lighter brown, beige, and black markings, including prominent eye
    spots. The larger brown butterfly appears to be feeding on the fruit.'
  - 'This prompt-driven custom interface feature is so powerful and easy to build
    (once you’ve figured out the gnarly details of browser sandboxing) that I expect
    it to show up as a feature in a wide range of products in 2025.

    Universal access to the best models lasted for just a few short months

    For a few short months this year all three of the best available models—GPT-4o,
    Claude 3.5 Sonnet and Gemini 1.5 Pro—were freely available to most of the world.'
  - 'Terminology aside, I remain skeptical as to their utility based, once again,
    on the challenge of gullibility. LLMs believe anything you tell them. Any systems
    that attempts to make meaningful decisions on your behalf will run into the same
    roadblock: how good is a travel agent, or a digital assistant, or even a research
    tool if it can’t distinguish truth from fiction?

    Just the other day Google Search was caught serving up an entirely fake description
    of the non-existant movie “Encanto 2”. It turned out to be summarizing an imagined
    movie listing from a fan fiction wiki.'
- source_sentence: 'content="1. What are the limitations of Apple''s LLM features
    compared to frontier LLMs, according to the context?\n2. What new shape of LLM
    was introduced in the final quarter of 2024, and what were the names of the initial
    models released?" additional_kwargs={''refusal'': None} response_metadata={''token_usage'':
    {''completion_tokens'': 54, ''prompt_tokens'': 195, ''total_tokens'': 249, ''completion_tokens_details'':
    {''accepted_prediction_tokens'': 0, ''audio_tokens'': 0, ''reasoning_tokens'':
    0, ''rejected_prediction_tokens'': 0}, ''prompt_tokens_details'': {''audio_tokens'':
    0, ''cached_tokens'': 0}}, ''model_name'': ''gpt-4o-mini-2024-07-18'', ''system_fingerprint'':
    ''fp_13eed4fce1'', ''finish_reason'': ''stop'', ''logprobs'': None} id=''run-f630b70d-5485-465d-b603-7832f2d1dfe6-0''
    usage_metadata={''input_tokens'': 195, ''output_tokens'': 54, ''total_tokens'':
    249, ''input_token_details'': {''audio'': 0, ''cache_read'': 0}, ''output_token_details'':
    {''audio'': 0, ''reasoning'': 0}}'
  sentences:
  - '17th: AI for Data Journalism: demonstrating what we can do with this stuff right
    now


    22nd: Options for accessing Llama 3 from the terminal using LLM




    May


    8th: Slop is the new name for unwanted AI-generated content


    15th: ChatGPT in “4o” mode is not running the new features yet


    29th: Training is not the same as chatting: ChatGPT and other LLMs don’t remember
    everything you say




    June


    6th: Accidental prompt injection against RAG applications


    10th: Thoughts on the WWDC 2024 keynote on Apple Intelligence


    17th: Language models on the command-line


    21st: Building search-based RAG using Claude, Datasette and Val Town


    27th: Open challenges for AI engineering




    July


    14th: Imitation Intelligence, my keynote for PyCon US 2024'
  - 'Now that those features are rolling out they’re pretty weak. As an LLM power-user
    I know what these models are capable of, and Apple’s LLM features offer a pale
    imitation of what a frontier LLM can do. Instead we’re getting notification summaries
    that misrepresent news headlines and writing assistant tools that I’ve not found
    useful at all. Genmoji are kind of fun though.

    The rise of inference-scaling “reasoning” models

    The most interesting development in the final quarter of 2024 was the introduction
    of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as
    o1-preview and o1-mini on September 12th.'
  - 'The models may have got more capable, but most of the limitations remained the
    same. OpenAI’s o1 may finally be able to (mostly) count the Rs in strawberry,
    but its abilities are still limited by its nature as an LLM and the constraints
    placed on it by the harness it’s running in. o1 can’t run web searches or use
    Code Interpreter, but GPT-4o can—both in that same ChatGPT UI. (o1 will pretend
    to do those things if you ask it to, a regression to the URL hallucinations bug
    from early 2023).

    What are we doing about this? Not much. Most users are thrown in at the deep end.
    The default LLM chat UI is like taking brand new computer users, dropping them
    into a Linux terminal and expecting them to figure it all out.'
- source_sentence: 'content=''1. What is the significance of the cost reduction mentioned
    in the context regarding LLMs in 2024?  \n2. How does the emergence of multi-modal
    LLMs, such as GPT-4 Vision and Google’s Gemini 1.0, reflect trends in technology
    for 2024?'' additional_kwargs={''refusal'': None} response_metadata={''token_usage'':
    {''completion_tokens'': 62, ''prompt_tokens'': 228, ''total_tokens'': 290, ''completion_tokens_details'':
    {''accepted_prediction_tokens'': 0, ''audio_tokens'': 0, ''reasoning_tokens'':
    0, ''rejected_prediction_tokens'': 0}, ''prompt_tokens_details'': {''audio_tokens'':
    0, ''cached_tokens'': 0}}, ''model_name'': ''gpt-4o-mini-2024-07-18'', ''system_fingerprint'':
    ''fp_00428b782a'', ''finish_reason'': ''stop'', ''logprobs'': None} id=''run-adc45d9c-270d-48b7-a80a-3f2de9b1f50c-0''
    usage_metadata={''input_tokens'': 228, ''output_tokens'': 62, ''total_tokens'':
    290, ''input_token_details'': {''audio'': 0, ''cache_read'': 0}, ''output_token_details'':
    {''audio'': 0, ''reasoning'': 0}}'
  sentences:
  - The most recent twist, again from December (December was a lot) is live video.
    ChatGPT voice mode now provides the option to share your camera feed with the
    model and talk about what you can see in real time. Google Gemini have a preview
    of the same feature, which they managed to ship the day before ChatGPT did.
  - '260 input tokens, 92 output tokens. Cost approximately 0.0024 cents (that’s less
    than a 400th of a cent).

    This increase in efficiency and reduction in price is my single favourite trend
    from 2024. I want the utility of LLMs at a fraction of the energy cost and it
    looks like that’s what we’re getting.

    Multimodal vision is common, audio and video are starting to emerge

    My butterfly example above illustrates another key trend from 2024: the rise of
    multi-modal LLMs.

    A year ago the single most notable example of these was GPT-4 Vision, released
    at OpenAI’s DevDay in November 2023. Google’s multi-modal Gemini 1.0 was announced
    on December 7th 2023 so it also (just) makes it into the 2023 window.'
  - 'Stuff we figured out about AI in 2023






















    Simon Willison’s Weblog

    Subscribe







    Stuff we figured out about AI in 2023

    31st December 2023

    2023 was the breakthrough year for Large Language Models (LLMs). I think it’s
    OK to call these AI—they’re the latest and (currently) most interesting development
    in the academic field of Artificial Intelligence that dates back to the 1950s.

    Here’s my attempt to round up the highlights in one place!'
- source_sentence: 'content="1. When did Google release their gemini-2.0-flash-thinking-exp
    model?\n2. What is the license under which Alibaba''s QwQ model was released?"
    additional_kwargs={''refusal'': None} response_metadata={''token_usage'': {''completion_tokens'':
    37, ''prompt_tokens'': 204, ''total_tokens'': 241, ''completion_tokens_details'':
    {''accepted_prediction_tokens'': 0, ''audio_tokens'': 0, ''reasoning_tokens'':
    0, ''rejected_prediction_tokens'': 0}, ''prompt_tokens_details'': {''audio_tokens'':
    0, ''cached_tokens'': 0}}, ''model_name'': ''gpt-4o-mini-2024-07-18'', ''system_fingerprint'':
    ''fp_00428b782a'', ''finish_reason'': ''stop'', ''logprobs'': None} id=''run-e70ae064-7316-41f9-9ee6-881279b91ba6-0''
    usage_metadata={''input_tokens'': 204, ''output_tokens'': 37, ''total_tokens'':
    241, ''input_token_details'': {''audio'': 0, ''cache_read'': 0}, ''output_token_details'':
    {''audio'': 0, ''reasoning'': 0}}'
  sentences:
  - Structured and Gradual Learning. In organic datasets, the relationship between
    tokens is often complex and indirect. Many reasoning steps may be required to
    connect the current token to the next, making it challenging for the model to
    learn effectively from next-token prediction. By contrast, each token generated
    by a language model is by definition predicted by the preceding tokens, making
    it easier for a model to follow the resulting reasoning patterns.
  - 'I think people who complain that LLM improvement has slowed are often missing
    the enormous advances in these multi-modal models. Being able to run prompts against
    images (and audio and video) is a fascinating new way to apply these models.

    Voice and live camera mode are science fiction come to life

    The audio and live video modes that have started to emerge deserve a special mention.

    The ability to talk to ChatGPT first arrived in September 2023, but it was mostly
    an illusion: OpenAI used their excellent Whisper speech-to-text model and a new
    text-to-speech model (creatively named tts-1) to enable conversations with the
    ChatGPT mobile apps, but the actual model just saw text.'
  - 'OpenAI are not the only game in town here. Google released their first entrant
    in the category, gemini-2.0-flash-thinking-exp, on December 19th.

    Alibaba’s Qwen team released their QwQ model on November 28th—under an Apache
    2.0 license, and that one I could run on my own machine. They followed that up
    with a vision reasoning model called QvQ on December 24th, which I also ran locally.

    DeepSeek made their DeepSeek-R1-Lite-Preview model available to try out through
    their chat interface on November 20th.

    To understand more about inference scaling I recommend Is AI progress slowing
    down? by Arvind Narayanan and Sayash Kapoor.'
- source_sentence: 'content=''1. What challenges did the author face last year regarding
    their choice of platform for machine learning models?  \n2. How does the author
    feel about their current experience as a Mac user compared to the previous year?''
    additional_kwargs={''refusal'': None} response_metadata={''token_usage'': {''completion_tokens'':
    43, ''prompt_tokens'': 188, ''total_tokens'': 231, ''completion_tokens_details'':
    {''accepted_prediction_tokens'': 0, ''audio_tokens'': 0, ''reasoning_tokens'':
    0, ''rejected_prediction_tokens'': 0}, ''prompt_tokens_details'': {''audio_tokens'':
    0, ''cached_tokens'': 0}}, ''model_name'': ''gpt-4o-mini-2024-07-18'', ''system_fingerprint'':
    ''fp_00428b782a'', ''finish_reason'': ''stop'', ''logprobs'': None} id=''run-0a628013-970b-4c1e-a336-e835eea1979e-0''
    usage_metadata={''input_tokens'': 188, ''output_tokens'': 43, ''total_tokens'':
    231, ''input_token_details'': {''audio'': 0, ''cache_read'': 0}, ''output_token_details'':
    {''audio'': 0, ''reasoning'': 0}}'
  sentences:
  - 'I’m still trying to figure out the best patterns for doing this for my own work.
    Everyone knows that evals are important, but there remains a lack of great guidance
    for how to best implement them—I’m tracking this under my evals tag. My SVG pelican
    riding a bicycle benchmark is a pale imitation of what a real eval suite should
    look like.

    Apple Intelligence is bad, Apple’s MLX library is excellent

    As a Mac user I’ve been feeling a lot better about my choice of platform this
    year.

    Last year it felt like my lack of a Linux/Windows  machine with an NVIDIA GPU
    was a huge disadvantage in terms of trying out new models.'
  - 'The GPT-4 barrier was comprehensively broken

    Some of those GPT-4 models run on my laptop

    LLM prices crashed, thanks to competition and increased efficiency

    Multimodal vision is common, audio and video are starting to emerge

    Voice and live camera mode are science fiction come to life

    Prompt driven app generation is a commodity already

    Universal access to the best models lasted for just a few short months

    “Agents” still haven’t really happened yet

    Evals really matter

    Apple Intelligence is bad, Apple’s MLX library is excellent

    The rise of inference-scaling “reasoning” models

    Was the best currently available LLM trained in China for less than $6m?

    The environmental impact got better

    The environmental impact got much, much worse'
  - 'The May 13th announcement of GPT-4o included a demo of a brand new voice mode,
    where the true multi-modal GPT-4o (the o is for “omni”) model could accept audio
    input and output incredibly realistic sounding speech without needing separate
    TTS or STT models.

    The demo also sounded conspicuously similar to Scarlett Johansson... and after
    she complained the voice from the demo, Skye, never made it to a production product.

    The delay in releasing the new voice mode after the initial demo caused quite
    a lot of confusion. I wrote about that in ChatGPT in “4o” mode is not running
    the new features yet.'
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.9166666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9166666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9166666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3055555555555555
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.9166666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9166666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9507303902211639
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9354166666666667
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9354166666666667
      name: Cosine Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tabesink92/legal-ft-v0")
# Run inference
sentences = [
    "content='1. What challenges did the author face last year regarding their choice of platform for machine learning models?  \\n2. How does the author feel about their current experience as a Mac user compared to the previous year?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 43, 'prompt_tokens': 188, 'total_tokens': 231, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_00428b782a', 'finish_reason': 'stop', 'logprobs': None} id='run-0a628013-970b-4c1e-a336-e835eea1979e-0' usage_metadata={'input_tokens': 188, 'output_tokens': 43, 'total_tokens': 231, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}",
    'I’m still trying to figure out the best patterns for doing this for my own work. Everyone knows that evals are important, but there remains a lack of great guidance for how to best implement them—I’m tracking this under my evals tag. My SVG pelican riding a bicycle benchmark is a pale imitation of what a real eval suite should look like.\nApple Intelligence is bad, Apple’s MLX library is excellent\nAs a Mac user I’ve been feeling a lot better about my choice of platform this year.\nLast year it felt like my lack of a Linux/Windows  machine with an NVIDIA GPU was a huge disadvantage in terms of trying out new models.',
    'The May 13th announcement of GPT-4o included a demo of a brand new voice mode, where the true multi-modal GPT-4o (the o is for “omni”) model could accept audio input and output incredibly realistic sounding speech without needing separate TTS or STT models.\nThe demo also sounded conspicuously similar to Scarlett Johansson... and after she complained the voice from the demo, Skye, never made it to a production product.\nThe delay in releasing the new voice mode after the initial demo caused quite a lot of confusion. I wrote about that in ChatGPT in “4o” mode is not running the new features yet.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Information Retrieval

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9167     |
| cosine_accuracy@3   | 0.9167     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9167     |
| cosine_precision@3  | 0.3056     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9167     |
| cosine_recall@3     | 0.9167     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9507** |
| cosine_mrr@10       | 0.9354     |
| cosine_map@100      | 0.9354     |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 156 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 156 samples:
  |         | sentence_0                                                                            | sentence_1                                                                          |
  |:--------|:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                              |
  | details | <ul><li>min: 343 tokens</li><li>mean: 356.73 tokens</li><li>max: 381 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 130.5 tokens</li><li>max: 204 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>content='1. What key themes and pivotal moments in the field of Large Language Models were identified in 2024?  \n2. How does the review of 2024 compare to the review of 2023 regarding advancements in LLMs?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 49, 'prompt_tokens': 159, 'total_tokens': 208, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_00428b782a', 'finish_reason': 'stop', 'logprobs': None} id='run-17232a8b-9c7d-4707-92bf-2701b977df50-0' usage_metadata={'input_tokens': 159, 'output_tokens': 49, 'total_tokens': 208, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}</code>                       | <code>Things we learned about LLMs in 2024<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Things we learned about LLMs in 2024<br>31st December 2024<br>A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.<br>This is a sequel to my review of 2023.<br>In this article:</code>                                                                                                                                                                                                                                         |
  | <code>content='1. What key themes and pivotal moments in the field of Large Language Models were identified in 2024?  \n2. How does the review of 2024 compare to the review of 2023 regarding advancements in LLMs?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 49, 'prompt_tokens': 159, 'total_tokens': 208, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_00428b782a', 'finish_reason': 'stop', 'logprobs': None} id='run-d5124245-02eb-4bc7-8286-0e8ebff06093-0' usage_metadata={'input_tokens': 159, 'output_tokens': 49, 'total_tokens': 208, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}</code>                       | <code>Things we learned about LLMs in 2024<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Things we learned about LLMs in 2024<br>31st December 2024<br>A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.<br>This is a sequel to my review of 2023.<br>In this article:</code>                                                                                                                                                                                                                                         |
  | <code>content='1. What advancements have been made in multimodal vision and audio/video capabilities in LLMs?\n2. How has the competition affected the pricing of LLMs and what impact did it have on universal access to the best models?' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 48, 'prompt_tokens': 210, 'total_tokens': 258, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_13eed4fce1', 'finish_reason': 'stop', 'logprobs': None} id='run-fb650155-719f-43bf-9b42-d01e282a04d5-0' usage_metadata={'input_tokens': 210, 'output_tokens': 48, 'total_tokens': 258, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}}</code> | <code>The GPT-4 barrier was comprehensively broken<br>Some of those GPT-4 models run on my laptop<br>LLM prices crashed, thanks to competition and increased efficiency<br>Multimodal vision is common, audio and video are starting to emerge<br>Voice and live camera mode are science fiction come to life<br>Prompt driven app generation is a commodity already<br>Universal access to the best models lasted for just a few short months<br>“Agents” still haven’t really happened yet<br>Evals really matter<br>Apple Intelligence is bad, Apple’s MLX library is excellent<br>The rise of inference-scaling “reasoning” models<br>Was the best currently available LLM trained in China for less than $6m?<br>The environmental impact got better<br>The environmental impact got much, much worse</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| 1.0   | 16   | 0.9638         |
| 2.0   | 32   | 0.9330         |
| 3.0   | 48   | 0.9156         |
| 3.125 | 50   | 0.9156         |
| 4.0   | 64   | 0.9156         |
| 5.0   | 80   | 0.9489         |
| 6.0   | 96   | 0.9507         |
| 6.25  | 100  | 0.9489         |
| 7.0   | 112  | 0.9507         |
| 8.0   | 128  | 0.9526         |
| 9.0   | 144  | 0.9507         |
| 9.375 | 150  | 0.9507         |
| 10.0  | 160  | 0.9507         |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.1
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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