How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prajwalJumde/outputs:# Run inference directly in the terminal:
llama cli -hf prajwalJumde/outputs: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 prajwalJumde/outputs:# Run inference directly in the terminal:
./llama-cli -hf prajwalJumde/outputs: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 prajwalJumde/outputs:# Run inference directly in the terminal:
./build/bin/llama-cli -hf prajwalJumde/outputs:Use Docker
docker model run hf.co/prajwalJumde/outputs:Quick Links
outputs
This model is a fine-tuned version of unsloth/orpheus-3b-0.1-ft 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 500
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Downloads last month
- 57
Hardware compatibility
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Model tree for prajwalJumde/outputs
Base model
meta-llama/Llama-3.2-3B-Instruct Finetuned
canopylabs/orpheus-3b-0.1-pretrained Finetuned
canopylabs/orpheus-3b-0.1-ft Finetuned
unsloth/orpheus-3b-0.1-ft
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf prajwalJumde/outputs:# Run inference directly in the terminal: llama cli -hf prajwalJumde/outputs: