Instructions to use jaswanthsanjay88/mini_embedding_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Atomic Chat new
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

mini_embedding_lora / config.json

jaswanthsanjay88

Add new SentenceTransformer model

d34597f verified 28 days ago

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	{
	"architectures": [
	"BertModel"
	],
	"attention_probs_dropout_prob": 0.1,
	"classifier_dropout": null,
	"dtype": "float16",
	"gradient_checkpointing": false,
	"hidden_act": "gelu",
	"hidden_dropout_prob": 0.1,
	"hidden_size": 384,
	"initializer_range": 0.02,
	"intermediate_size": 1536,
	"layer_norm_eps": 1e-12,
	"max_position_embeddings": 512,
	"model_type": "bert",
	"num_attention_heads": 12,
	"num_hidden_layers": 6,
	"pad_token_id": 0,
	"position_embedding_type": "absolute",
	"transformers_version": "4.56.2",
	"type_vocab_size": 2,
	"use_cache": true,
	"vocab_size": 30522
	}