Instructions to use Striker-7/finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Striker-7/finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Striker-7/finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Striker-7/finetuned") model = AutoModelForCausalLM.from_pretrained("Striker-7/finetuned") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Striker-7/finetuned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Striker-7/finetuned" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Striker-7/finetuned", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Striker-7/finetuned
- SGLang
How to use Striker-7/finetuned 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 "Striker-7/finetuned" \ --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": "Striker-7/finetuned", "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 "Striker-7/finetuned" \ --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": "Striker-7/finetuned", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Striker-7/finetuned with Docker Model Runner:
docker model run hf.co/Striker-7/finetuned
finetuned
finetuned is an SFT fine-tuned version of EleutherAI/gpt-neo-2.7B using a custom training dataset. This model was made with Phinetune
Process
- Learning Rate: 0.0001
- Maximum Sequence Length: 2048
- Dataset: Striker-7/mattxt
- Split: train
馃捇 Usage
!pip install -qU transformers
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model = "Striker-7/finetuned"
tokenizer = AutoTokenizer.from_pretrained(model)
# Example prompt
prompt = "Your example prompt here"
# Generate a response
model = AutoModelForCausalLM.from_pretrained(model)
pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
outputs = pipeline(prompt, max_length=50, num_return_sequences=1)
print(outputs[0]["generated_text"])
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