Instructions to use 34574rd/edge_llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 34574rd/edge_llm with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="34574rd/edge_llm",
	filename="small_text_llm[1].gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use 34574rd/edge_llm with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 34574rd/edge_llm
# Run inference directly in the terminal:
llama-cli -hf 34574rd/edge_llm

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 34574rd/edge_llm
# Run inference directly in the terminal:
llama-cli -hf 34574rd/edge_llm

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 34574rd/edge_llm
# Run inference directly in the terminal:
./llama-cli -hf 34574rd/edge_llm

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 34574rd/edge_llm
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 34574rd/edge_llm

Use Docker

docker model run hf.co/34574rd/edge_llm

LM Studio
Jan
Ollama
How to use 34574rd/edge_llm with Ollama:
```
ollama run hf.co/34574rd/edge_llm
```

Unsloth Studio new

How to use 34574rd/edge_llm 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 34574rd/edge_llm 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 34574rd/edge_llm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 34574rd/edge_llm to start chatting

Docker Model Runner
How to use 34574rd/edge_llm with Docker Model Runner:
```
docker model run hf.co/34574rd/edge_llm
```

Lemonade

How to use 34574rd/edge_llm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 34574rd/edge_llm

Run and chat with the model

lemonade run user.edge_llm-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Atlas-1B: Lightweight Fine-tuned LLM for Edge and Low-Memory Devices

🚀 Atlas-1B is a 1.2-billion parameter model fine-tuned from BaseLLM-1B to deliver improved accuracy, reasoning, and efficiency on low-power inference devices (e.g., Jetson, Ryzen APU, and mobile-based LLM frameworks).
This version introduces quantization-aware finetuning, dataset specialization, and token efficiency optimization, making it a solid drop-in model for on-device AI use cases.

🧠 Model Overview

Base model: BaseLLM-1B v1.3 (transformer-based autoregressive)
Architecture: Decoder-only transformer
Parameters: 1.2B
Precision support: FP16 / INT8 / INT4
Context length: 16K tokens
Tokenizer: SentencePiece (32K vocab)
Frameworks supported: PyTorch, vLLM, and sglang

This model was optimized specifically for edge inference and multi-request throughput, providing ~30% lower memory bandwidth usage at batch=4 compared to the base model.

🧩 Use Cases

On-device chat assistants
Smart IoT response systems
Embedded analytics (offline summarization, intent detection, etc.)
Lightweight reasoning for robotics

🔧 Fine-tuning Details

Attribute	Description
Dataset	Blend of 50M tokens curated for code, chat, and reasoning
Training framework	PyTorch + DeepSpeed ZeRO-2
Optimizer	AdamW
Learning rate	2e-5 (cosine decay)
Batch size	512 tokens per GPU
Epochs	3
Loss function	Cross-entropy (token-level)
Special techniques	LoRA adapters (rank=8), QLoRA-aware finetuning, FlashAttention-2 integration

🧪 Performance Benchmarks

Metric	BaseLLM-1B	Atlas-1B
MMLU (Subset)	30.2	38.7
CodeEval (Python)	22.4	29.1
Average latency (Jetson Orin, INT4)	213ms	158ms
Memory usage (FP16)	7.9GB	5.4GB

Benchmarks measured with vLLM 0.4.2 and sglang backend on an RTX 3060 (12GB) and Jetson Orin AGX.

Downloads last month: 21

GGUF

Model size

1B params

Architecture

olmo

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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