Text Generation
Transformers
Safetensors
GGUF
starcoder2
Generated from Trainer
text-generation-inference
Instructions to use gubee/fine-tuned-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gubee/fine-tuned-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gubee/fine-tuned-model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gubee/fine-tuned-model") model = AutoModelForCausalLM.from_pretrained("gubee/fine-tuned-model") - llama-cpp-python
How to use gubee/fine-tuned-model with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="gubee/fine-tuned-model", filename="fine-tuned-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 gubee/fine-tuned-model with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf gubee/fine-tuned-model # Run inference directly in the terminal: llama-cli -hf gubee/fine-tuned-model
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf gubee/fine-tuned-model # Run inference directly in the terminal: llama-cli -hf gubee/fine-tuned-model
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 gubee/fine-tuned-model # Run inference directly in the terminal: ./llama-cli -hf gubee/fine-tuned-model
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 gubee/fine-tuned-model # Run inference directly in the terminal: ./build/bin/llama-cli -hf gubee/fine-tuned-model
Use Docker
docker model run hf.co/gubee/fine-tuned-model
- LM Studio
- Jan
- vLLM
How to use gubee/fine-tuned-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gubee/fine-tuned-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gubee/fine-tuned-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gubee/fine-tuned-model
- SGLang
How to use gubee/fine-tuned-model with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "gubee/fine-tuned-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gubee/fine-tuned-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "gubee/fine-tuned-model" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gubee/fine-tuned-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use gubee/fine-tuned-model with Ollama:
ollama run hf.co/gubee/fine-tuned-model
- Unsloth Studio new
How to use gubee/fine-tuned-model 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 gubee/fine-tuned-model 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 gubee/fine-tuned-model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for gubee/fine-tuned-model to start chatting
- Docker Model Runner
How to use gubee/fine-tuned-model with Docker Model Runner:
docker model run hf.co/gubee/fine-tuned-model
- Lemonade
How to use gubee/fine-tuned-model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gubee/fine-tuned-model
Run and chat with the model
lemonade run user.fine-tuned-model-{{QUANT_TAG}}List all available models
lemonade list
fine-tuned-model
This model is a fine-tuned version of bigcode/starcoder2-3b on the None 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cpu
- Datasets 3.3.0
- Tokenizers 0.21.0
- Downloads last month
- 3
Model tree for gubee/fine-tuned-model
Base model
bigcode/starcoder2-3b