---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:156
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: In what year does the author expect the prompt-driven custom interface
feature to be widely integrated into products?
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'
- '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: What notable development in LLM technology occurred in the final
quarter of 2024?
sentences:
- '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.'
- '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.'
- '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.'
- source_sentence: What is the license under which Alibaba's QwQ model was released?
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.
- '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.'
- '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: What is the significance of the cost reduction mentioned in the
context regarding LLMs in 2024?
sentences:
- '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.'
- 'I like people who are skeptical of this stuff. The hype has been deafening for
more than two years now, and there are enormous quantities of snake oil and misinformation
out there. A lot of very bad decisions are being made based on that hype. Being
critical is a virtue.
If we want people with decision-making authority to make good decisions about
how to apply these tools we first need to acknowledge that there ARE good applications,
and then help explain how to put those into practice while avoiding the many unintiutive
traps.
(If you still don’t think there are any good applications at all I’m not sure
why you made it to this point in the article!)'
- '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.'
- source_sentence: How does the author feel about their choice of platform as a Mac
user this year compared to last year?
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'
- 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.
pipeline_tag: sentence-similarity
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
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: 0.7916666666666666
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
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: 0.7916666666666666
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3333333333333333
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: 0.7916666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
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: 0.923110365327387
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8958333333333334
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8958333333333334
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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
### 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("thomfoolery/legal-ft-v0")
# Run inference
sentences = [
'How does the author feel about their choice of platform as a Mac user this year compared to last year?',
'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.',
'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.',
]
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]
```
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7917 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.7917 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.7917 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9231** |
| cosine_mrr@10 | 0.8958 |
| cosine_map@100 | 0.8958 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 156 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 156 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 14 tokens
- mean: 20.66 tokens
- max: 35 tokens
| - min: 43 tokens
- mean: 130.44 tokens
- max: 204 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What key themes and pivotal moments in the field of Large Language Models were identified in 2024? | Things we learned about LLMs in 2024
Simon Willison’s Weblog
Subscribe
Things we learned about LLMs in 2024
31st December 2024
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.
This is a sequel to my review of 2023.
In this article: |
| How does the review of 2024 compare to the previous year's review of 2023? | Things we learned about LLMs in 2024
Simon Willison’s Weblog
Subscribe
Things we learned about LLMs in 2024
31st December 2024
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.
This is a sequel to my review of 2023.
In this article: |
| What advancements have been made in multimodal vision and audio/video capabilities in LLMs? | 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 |
* Loss: [MatryoshkaLoss](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
Click to expand
- `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