Text Generation
Transformers
Safetensors
llama
alignment-handbook
Generated from Trainer
text-generation-inference
Instructions to use fblgit/una-llama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fblgit/una-llama-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fblgit/una-llama-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fblgit/una-llama-7b") model = AutoModelForCausalLM.from_pretrained("fblgit/una-llama-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fblgit/una-llama-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fblgit/una-llama-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fblgit/una-llama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fblgit/una-llama-7b
- SGLang
How to use fblgit/una-llama-7b 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 "fblgit/una-llama-7b" \ --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": "fblgit/una-llama-7b", "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 "fblgit/una-llama-7b" \ --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": "fblgit/una-llama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fblgit/una-llama-7b with Docker Model Runner:
docker model run hf.co/fblgit/una-llama-7b
una-llama-7b
UNA: Uniform Neural Alignment It increases 6.75% the performance of the pre-trained base LLaMA (1) 7B.
This model is a fine-tuned version of huggyllama/llama-7b:
- Loss: 0.5529
- Rewards/chosen: 0.3633
- Rewards/rejected: -0.1873
- Rewards/accuracies: 0.7230
- Rewards/margins: 0.5506
- Logps/rejected: -217.7784
- Logps/chosen: -235.0354
- Logits/rejected: -0.7752
- Logits/chosen: -0.5259
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Framework versions
- Transformers 4.35.0-UNA
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
- Downloads last month
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Base model
huggyllama/llama-7b