all-MiniLM-L6-v2 GGUF Models
Choosing the Right Model Format
Selecting the correct model format depends on your hardware capabilities and memory constraints.
BF16 (Brain Float 16) β Use if BF16 acceleration is available
- A 16-bit floating-point format designed for faster computation while retaining good precision.
- Provides similar dynamic range as FP32 but with lower memory usage.
- Recommended if your hardware supports BF16 acceleration (check your deviceβs specs).
- Ideal for high-performance inference with reduced memory footprint compared to FP32.
π Use BF16 if:
β Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
β You want higher precision while saving memory.
β You plan to requantize the model into another format.
π Avoid BF16 if:
β Your hardware does not support BF16 (it may fall back to FP32 and run slower).
β You need compatibility with older devices that lack BF16 optimization.
F16 (Float 16) β More widely supported than BF16
- A 16-bit floating-point high precision but with less of range of values than BF16.
- Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
- Slightly lower numerical precision than BF16 but generally sufficient for inference.
π Use F16 if:
β Your hardware supports FP16 but not BF16.
β You need a balance between speed, memory usage, and accuracy.
β You are running on a GPU or another device optimized for FP16 computations.
π Avoid F16 if:
β Your device lacks native FP16 support (it may run slower than expected).
β You have memory limitations.
Quantized Models (Q4_K, Q6_K, Q8, etc.) β For CPU & Low-VRAM Inference
Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
- Lower-bit models (Q4_K) β Best for minimal memory usage, may have lower precision.
- Higher-bit models (Q6_K, Q8_0) β Better accuracy, requires more memory.
π Use Quantized Models if:
β You are running inference on a CPU and need an optimized model.
β Your device has low VRAM and cannot load full-precision models.
β You want to reduce memory footprint while keeping reasonable accuracy.
π Avoid Quantized Models if:
β You need maximum accuracy (full-precision models are better for this).
β Your hardware has enough VRAM for higher-precision formats (BF16/F16).
Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)
These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.
IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
- Use case: Best for ultra-low-memory devices where even Q4_K is too large.
- Trade-off: Lower accuracy compared to higher-bit quantizations.
IQ3_S: Small block size for maximum memory efficiency.
- Use case: Best for low-memory devices where IQ3_XS is too aggressive.
IQ3_M: Medium block size for better accuracy than IQ3_S.
- Use case: Suitable for low-memory devices where IQ3_S is too limiting.
Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
- Use case: Best for low-memory devices where Q6_K is too large.
Q4_0: Pure 4-bit quantization, optimized for ARM devices.
- Use case: Best for ARM-based devices or low-memory environments.
Summary Table: Model Format Selection
Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
---|---|---|---|---|
BF16 | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory |
F16 | High | High | FP16-supported devices | GPU inference when BF16 isnβt available |
Q4_K | Medium Low | Low | CPU or Low-VRAM devices | Best for memory-constrained environments |
Q6_K | Medium | Moderate | CPU with more memory | Better accuracy while still being quantized |
Q8_0 | High | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models |
IQ3_XS | Very Low | Very Low | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |
Q4_0 | Low | Low | ARM or low-memory devices | llama.cpp can optimize for ARM devices |
Included Files & Details
all-MiniLM-L6-v2-bf16.gguf
- Model weights preserved in BF16.
- Use this if you want to requantize the model into a different format.
- Best if your device supports BF16 acceleration.
all-MiniLM-L6-v2-f16.gguf
- Model weights stored in F16.
- Use if your device supports FP16, especially if BF16 is not available.
all-MiniLM-L6-v2-bf16-q8_0.gguf
- Output & embeddings remain in BF16.
- All other layers quantized to Q8_0.
- Use if your device supports BF16 and you want a quantized version.
all-MiniLM-L6-v2-f16-q8_0.gguf
- Output & embeddings remain in F16.
- All other layers quantized to Q8_0.
all-MiniLM-L6-v2-q4_k.gguf
- Output & embeddings quantized to Q8_0.
- All other layers quantized to Q4_K.
- Good for CPU inference with limited memory.
all-MiniLM-L6-v2-q4_k_s.gguf
- Smallest Q4_K variant, using less memory at the cost of accuracy.
- Best for very low-memory setups.
all-MiniLM-L6-v2-q6_k.gguf
- Output & embeddings quantized to Q8_0.
- All other layers quantized to Q6_K .
all-MiniLM-L6-v2-q8_0.gguf
- Fully Q8 quantized model for better accuracy.
- Requires more memory but offers higher precision.
all-MiniLM-L6-v2-iq3_xs.gguf
- IQ3_XS quantization, optimized for extreme memory efficiency.
- Best for ultra-low-memory devices.
all-MiniLM-L6-v2-iq3_m.gguf
- IQ3_M quantization, offering a medium block size for better accuracy.
- Suitable for low-memory devices.
all-MiniLM-L6-v2-q4_0.gguf
- Pure Q4_0 quantization, optimized for ARM devices.
- Best for low-memory environments.
- Prefer IQ4_NL for better accuracy.
π If you find these models useful
Please click like β€ . Also Iβd really appreciate it if you could test my Network Monitor Assistant at π Network Monitor Assitant.
π¬ Click the chat icon (bottom right of the main and dashboard pages) . Choose a LLM; toggle between the LLM Types TurboLLM -> FreeLLM -> TestLLM.
What I'm Testing
I'm experimenting with function calling against my network monitoring service. Using small open source models. I am into the question "How small can it go and still function".
π‘ TestLLM β Runs the current testing model using llama.cpp on 6 threads of a Cpu VM (Should take about 15s to load. Inference speed is quite slow and it only processes one user prompt at a timeβstill working on scaling!). If you're curious, I'd be happy to share how it works! .
The other Available AI Assistants
π’ TurboLLM β Uses gpt-4o-mini Fast! . Note: tokens are limited since OpenAI models are pricey, but you can Login or Download the Free Network Monitor agent to get more tokens, Alternatively use the TestLLM .
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all-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
embeddings = model.encode(sentences)
print(embeddings)
Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained nreimers/MiniLM-L6-H384-uncased
model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developed this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developed this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
Intended uses
Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
Training procedure
Pre-training
We use the pretrained nreimers/MiniLM-L6-H384-uncased
model. Please refer to the model card for more detailed information about the pre-training procedure.
Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs.
Hyper parameters
We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: train_script.py
.
Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the data_config.json
file.
Dataset | Paper | Number of training tuples |
---|---|---|
Reddit comments (2015-2018) | paper | 726,484,430 |
S2ORC Citation pairs (Abstracts) | paper | 116,288,806 |
WikiAnswers Duplicate question pairs | paper | 77,427,422 |
PAQ (Question, Answer) pairs | paper | 64,371,441 |
S2ORC Citation pairs (Titles) | paper | 52,603,982 |
S2ORC (Title, Abstract) | paper | 41,769,185 |
Stack Exchange (Title, Body) pairs | - | 25,316,456 |
Stack Exchange (Title+Body, Answer) pairs | - | 21,396,559 |
Stack Exchange (Title, Answer) pairs | - | 21,396,559 |
MS MARCO triplets | paper | 9,144,553 |
GOOAQ: Open Question Answering with Diverse Answer Types | paper | 3,012,496 |
Yahoo Answers (Title, Answer) | paper | 1,198,260 |
Code Search | - | 1,151,414 |
COCO Image captions | paper | 828,395 |
SPECTER citation triplets | paper | 684,100 |
Yahoo Answers (Question, Answer) | paper | 681,164 |
Yahoo Answers (Title, Question) | paper | 659,896 |
SearchQA | paper | 582,261 |
Eli5 | paper | 325,475 |
Flickr 30k | paper | 317,695 |
Stack Exchange Duplicate questions (titles) | 304,525 | |
AllNLI (SNLI and MultiNLI | paper SNLI, paper MultiNLI | 277,230 |
Stack Exchange Duplicate questions (bodies) | 250,519 | |
Stack Exchange Duplicate questions (titles+bodies) | 250,460 | |
Sentence Compression | paper | 180,000 |
Wikihow | paper | 128,542 |
Altlex | paper | 112,696 |
Quora Question Triplets | - | 103,663 |
Simple Wikipedia | paper | 102,225 |
Natural Questions (NQ) | paper | 100,231 |
SQuAD2.0 | paper | 87,599 |
TriviaQA | - | 73,346 |
Total | 1,170,060,424 |
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