Instructions to use Zintoulou/finetuningqvk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zintoulou/finetuningqvk1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zintoulou/finetuningqvk1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Zintoulou/finetuningqvk1") model = AutoModelForCausalLM.from_pretrained("Zintoulou/finetuningqvk1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Zintoulou/finetuningqvk1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zintoulou/finetuningqvk1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zintoulou/finetuningqvk1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Zintoulou/finetuningqvk1
- SGLang
How to use Zintoulou/finetuningqvk1 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 "Zintoulou/finetuningqvk1" \ --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": "Zintoulou/finetuningqvk1", "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 "Zintoulou/finetuningqvk1" \ --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": "Zintoulou/finetuningqvk1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Zintoulou/finetuningqvk1 with Docker Model Runner:
docker model run hf.co/Zintoulou/finetuningqvk1
finetuningqvk1
This model is a fine-tuned version of codellama/CodeLlama-7b-Instruct-hf on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.3415
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: 0.001
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.688 | 1.0 | 1 | 2.7638 |
| 2.275 | 2.0 | 2 | 2.2309 |
| 1.8141 | 3.0 | 3 | 1.9247 |
| 1.4948 | 4.0 | 4 | 1.6327 |
| 1.2029 | 5.0 | 5 | 1.4417 |
| 0.9743 | 6.0 | 6 | 1.3415 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.13.3
- Downloads last month
- 4
Model tree for Zintoulou/finetuningqvk1
Base model
codellama/CodeLlama-7b-Instruct-hf