Instructions to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF",
	filename="Lambda-Equulei-1.5B-xLingual.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_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 prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_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 prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF 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 prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF 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 prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lambda-Equulei-1.5B-xLingual-GGUF-Q4_K_M

List all available models

lemonade list

Lambda-Equulei-1.5B-xLingual

Lambda-Equulei-1.5B-xLingual is a multilingual conversational model fine-tuned from Qwen2-1.5B, specifically designed for cross-lingual chat and experimental conversations across 30+ languages. It brings advanced multilingual understanding and natural dialogue capabilities in a compact size, ideal for international communication tools, language learning platforms, and global conversational assistants.

Model Files

Filename	Size	Format	Description
Lambda-Equulei-1.5B-xLingual.BF16.gguf	3.56 GB	BF16	Brain Float 16-bit quantization
Lambda-Equulei-1.5B-xLingual.F16.gguf	3.56 GB	F16	Half precision (16-bit) floating point
Lambda-Equulei-1.5B-xLingual.F32.gguf	7.11 GB	F32	Full precision (32-bit) floating point
Lambda-Equulei-1.5B-xLingual.Q2_K.gguf	753 MB	Q2_K	2-bit quantization with K-quant
Lambda-Equulei-1.5B-xLingual.Q3_K_L.gguf	980 MB	Q3_K_L	3-bit quantization (Large) with K-quant
Lambda-Equulei-1.5B-xLingual.Q3_K_M.gguf	924 MB	Q3_K_M	3-bit quantization (Medium) with K-quant
Lambda-Equulei-1.5B-xLingual.Q3_K_S.gguf	861 MB	Q3_K_S	3-bit quantization (Small) with K-quant
Lambda-Equulei-1.5B-xLingual.Q4_K_M.gguf	1.12 GB	Q4_K_M	4-bit quantization (Medium) with K-quant
Lambda-Equulei-1.5B-xLingual.Q4_K_S.gguf	1.07 GB	Q4_K_S	4-bit quantization (Small) with K-quant
Lambda-Equulei-1.5B-xLingual.Q5_K_M.gguf	1.29 GB	Q5_K_M	5-bit quantization (Medium) with K-quant
Lambda-Equulei-1.5B-xLingual.Q5_K_S.gguf	1.26 GB	Q5_K_S	5-bit quantization (Small) with K-quant
Lambda-Equulei-1.5B-xLingual.Q6_K.gguf	1.46 GB	Q6_K	6-bit quantization with K-quant
Lambda-Equulei-1.5B-xLingual.Q8_0.gguf	1.89 GB	Q8_0	8-bit quantization

Recommended Usage

Q4_K_M or Q5_K_M: Best balance of quality and performance for most users
Q6_K or Q8_0: Higher quality, moderate file sizes
Q2_K or Q3_K_S: Fastest inference, lower quality (good for resource-constrained environments)
F16 or BF16: High quality, requires more VRAM
F32: Highest quality, requires significant VRAM

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 86

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF

Base model

Qwen/Qwen2.5-1.5B

Finetuned

prithivMLmods/Lambda-Equulei-1.5B-xLingual

Quantized

(3)

this model

Collection including prithivMLmods/Lambda-Equulei-1.5B-xLingual-GGUF

Blitzar Experimentals

Collection

coder • 4 items • Updated Apr 27 • 2