Text Generation
Transformers
Safetensors
llama
alignment-handbook
Generated from Trainer
text-generation-inference
How to use from
SGLangUse 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
}'Quick Links
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
- 16
Model tree for fblgit/una-llama-7b
Base model
huggyllama/llama-7b
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 }'