Instructions to use winglian/llama-neft-exp3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use winglian/llama-neft-exp3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="winglian/llama-neft-exp3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("winglian/llama-neft-exp3") model = AutoModelForCausalLM.from_pretrained("winglian/llama-neft-exp3") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use winglian/llama-neft-exp3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "winglian/llama-neft-exp3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "winglian/llama-neft-exp3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/winglian/llama-neft-exp3
- SGLang
How to use winglian/llama-neft-exp3 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 "winglian/llama-neft-exp3" \ --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": "winglian/llama-neft-exp3", "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 "winglian/llama-neft-exp3" \ --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": "winglian/llama-neft-exp3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use winglian/llama-neft-exp3 with Docker Model Runner:
docker model run hf.co/winglian/llama-neft-exp3
out
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2049
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: 3.8e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3761 | 0.01 | 1 | 1.4211 |
| 1.1922 | 0.2 | 14 | 1.2246 |
| 1.095 | 0.4 | 28 | 1.2137 |
| 1.1475 | 0.6 | 42 | 1.2152 |
| 1.1639 | 0.81 | 56 | 1.2224 |
| 1.0431 | 1.01 | 70 | 1.2131 |
| 0.9464 | 1.21 | 84 | 1.2100 |
| 1.1368 | 1.41 | 98 | 1.2060 |
| 1.0991 | 1.61 | 112 | 1.2022 |
| 0.9896 | 1.81 | 126 | 1.2014 |
| 0.9592 | 2.01 | 140 | 1.1991 |
| 0.9789 | 2.22 | 154 | 1.2054 |
| 1.0028 | 2.42 | 168 | 1.2048 |
| 0.9374 | 2.62 | 182 | 1.2051 |
| 0.9318 | 2.82 | 196 | 1.2049 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.14.0
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Model tree for winglian/llama-neft-exp3
Base model
meta-llama/Llama-2-7b-hf