Instructions to use raniero/submission_worker_hash_013 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raniero/submission_worker_hash_013 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="raniero/submission_worker_hash_013",
	filename="model.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use raniero/submission_worker_hash_013 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf raniero/submission_worker_hash_013
# Run inference directly in the terminal:
llama-cli -hf raniero/submission_worker_hash_013

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf raniero/submission_worker_hash_013
# Run inference directly in the terminal:
llama-cli -hf raniero/submission_worker_hash_013

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 raniero/submission_worker_hash_013
# Run inference directly in the terminal:
./llama-cli -hf raniero/submission_worker_hash_013

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 raniero/submission_worker_hash_013
# Run inference directly in the terminal:
./build/bin/llama-cli -hf raniero/submission_worker_hash_013

Use Docker

docker model run hf.co/raniero/submission_worker_hash_013

LM Studio
Jan
Ollama
How to use raniero/submission_worker_hash_013 with Ollama:
```
ollama run hf.co/raniero/submission_worker_hash_013
```

Unsloth Studio new

How to use raniero/submission_worker_hash_013 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 raniero/submission_worker_hash_013 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 raniero/submission_worker_hash_013 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for raniero/submission_worker_hash_013 to start chatting

Docker Model Runner
How to use raniero/submission_worker_hash_013 with Docker Model Runner:
```
docker model run hf.co/raniero/submission_worker_hash_013
```

Lemonade

How to use raniero/submission_worker_hash_013 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull raniero/submission_worker_hash_013

Run and chat with the model

lemonade run user.submission_worker_hash_013-{{QUANT_TAG}}

List all available models

lemonade list

Submission per task `submission_worker_hash_013`

Repo: submission_worker_hash_013
Timestamp: 2025-07-11T00:25:41.404316Z
SHA256: FILE NON TROVATO

Downloads last month: 2

Safetensors

Model size

4.19M params

Tensor type

F32

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for raniero/submission_worker_hash_013

Base model

meta-llama/Llama-2-7b-chat-hf

Quantized

(102)

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Submission per task submission_worker_hash_013

Model tree for raniero/submission_worker_hash_013

Submission per task `submission_worker_hash_013`