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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="raniero/submission_test_loss_curve_final",
	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_test_loss_curve_final 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_test_loss_curve_final
# Run inference directly in the terminal:
llama-cli -hf raniero/submission_test_loss_curve_final

Install from WinGet (Windows)

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

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

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

Use Docker

docker model run hf.co/raniero/submission_test_loss_curve_final

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

Unsloth Studio new

How to use raniero/submission_test_loss_curve_final 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_test_loss_curve_final 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_test_loss_curve_final 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_test_loss_curve_final to start chatting

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

Lemonade

How to use raniero/submission_test_loss_curve_final with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.submission_test_loss_curve_final-{{QUANT_TAG}}

List all available models

lemonade list

Submission for task `submission_test_loss_curve_final`

Task ID: sim-task-readme-loss-final
Repo: submission_test_loss_curve_final
Loss: 4.868357499440511
Timestamp: 2025-06-30T23:42:52.068677

LoRA Parameters

Rank: 8
Alpha: 16
Dropout: 0.1
Epochs: 1
Learning rate: 1e-4
Batch size: 1

Example

Instruction:
Come si calcola la derivata di ( x^2 )?

Response:
La derivata di ( x^2 ) rispetto a ( x ) è ( 2x ).

Training Info

This LoRA adapter was trained using a dataset dynamically generated from RAG (FAISS index) based on the task theme.
The dataset consisted of 35 prompt-response examples related to mathematics and programming.

The adapter was trained on CPU in ~7 minutes and uploaded automatically via Hugging Face API.

Training Curve

License and Usage

This adapter inherits the license and intended usage of the base model Meta LLaMA 2 (Meta AI License),
and is provided here for research purposes only as part of the Bittensor Subnet 56 participation.

Downloads last month: 2

Safetensors

Model size

4.19M params

Tensor type

F32

Inference Providers NEW

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Model tree for raniero/submission_test_loss_curve_final

Base model

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

Quantized

(74)

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