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

Libraries

How to use binayakkoirala/outputs with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "binayakkoirala/outputs")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use binayakkoirala/outputs 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 binayakkoirala/outputs:Q8_0
# Run inference directly in the terminal:
llama cli -hf binayakkoirala/outputs:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf binayakkoirala/outputs:Q8_0
# Run inference directly in the terminal:
llama cli -hf binayakkoirala/outputs:Q8_0

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 binayakkoirala/outputs:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf binayakkoirala/outputs:Q8_0

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 binayakkoirala/outputs:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf binayakkoirala/outputs:Q8_0

Use Docker

docker model run hf.co/binayakkoirala/outputs:Q8_0

LM Studio
Jan
Ollama
How to use binayakkoirala/outputs with Ollama:
```
ollama run hf.co/binayakkoirala/outputs:Q8_0
```

Unsloth Studio

How to use binayakkoirala/outputs 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 binayakkoirala/outputs 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 binayakkoirala/outputs to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use binayakkoirala/outputs with Docker Model Runner:
```
docker model run hf.co/binayakkoirala/outputs:Q8_0
```

Lemonade

How to use binayakkoirala/outputs with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull binayakkoirala/outputs:Q8_0

Run and chat with the model

lemonade run user.outputs-Q8_0

List all available models

lemonade list

Atomic Chat

outputs

This model is a fine-tuned version of unsloth/mistral-7b-bnb-4bit on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 8
seed: 3407
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
training_steps: 60
mixed_precision_training: Native AMP

Training results

Framework versions

PEFT 0.11.1
Transformers 4.41.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 9

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

8-bit

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Model tree for binayakkoirala/outputs

Base model

unsloth/mistral-7b-bnb-4bit

Adapter

(107)

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