Sentence Similarity
sentence-transformers
Safetensors
GGUF
English
bert
feature-extraction
Generated from Trainer
dataset_size:100000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use jaswanthsanjay88/mini_embedding_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jaswanthsanjay88/mini_embedding_lora with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jaswanthsanjay88/mini_embedding_lora") sentences = [ "the three boys are all holding onto a flotation device in the water.", "Three boys are in a body of water.", "A school band is playing.", "There is an animal in water" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jaswanthsanjay88/mini_embedding_lora with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M # Run inference directly in the terminal: llama cli -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M # Run inference directly in the terminal: llama cli -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf jaswanthsanjay88/mini_embedding_lora:Q5_K_M
Use Docker
docker model run hf.co/jaswanthsanjay88/mini_embedding_lora:Q5_K_M
- LM Studio
- Jan
- Ollama
How to use jaswanthsanjay88/mini_embedding_lora with Ollama:
ollama run hf.co/jaswanthsanjay88/mini_embedding_lora:Q5_K_M
- Unsloth Studio
How to use jaswanthsanjay88/mini_embedding_lora with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jaswanthsanjay88/mini_embedding_lora to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jaswanthsanjay88/mini_embedding_lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jaswanthsanjay88/mini_embedding_lora to start chatting
- Docker Model Runner
How to use jaswanthsanjay88/mini_embedding_lora with Docker Model Runner:
docker model run hf.co/jaswanthsanjay88/mini_embedding_lora:Q5_K_M
- Lemonade
How to use jaswanthsanjay88/mini_embedding_lora with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jaswanthsanjay88/mini_embedding_lora:Q5_K_M
Run and chat with the model
lemonade run user.mini_embedding_lora-Q5_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,055 Bytes
d34597f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | {
"alora_invocation_tokens": null,
"alpha_pattern": {},
"arrow_config": null,
"auto_mapping": null,
"base_model_name_or_path": "unsloth/all-MiniLM-L6-v2",
"bias": "none",
"corda_config": null,
"ensure_weight_tying": false,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 128,
"lora_bias": false,
"lora_dropout": 0,
"lora_ga_config": null,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"peft_version": "0.19.1",
"qalora_group_size": 16,
"r": 64,
"rank_pattern": {},
"revision": null,
"target_modules": [
"key",
"value",
"dense",
"query"
],
"target_parameters": null,
"task_type": "FEATURE_EXTRACTION",
"trainable_token_indices": null,
"use_bdlora": null,
"use_dora": false,
"use_qalora": false,
"use_rslora": false
} |