Instructions to use openlm-research/open_llama_3b_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openlm-research/open_llama_3b_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openlm-research/open_llama_3b_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_3b_v2") model = AutoModelForCausalLM.from_pretrained("openlm-research/open_llama_3b_v2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use openlm-research/open_llama_3b_v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openlm-research/open_llama_3b_v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openlm-research/open_llama_3b_v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openlm-research/open_llama_3b_v2
- SGLang
How to use openlm-research/open_llama_3b_v2 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 "openlm-research/open_llama_3b_v2" \ --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": "openlm-research/open_llama_3b_v2", "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 "openlm-research/open_llama_3b_v2" \ --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": "openlm-research/open_llama_3b_v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openlm-research/open_llama_3b_v2 with Docker Model Runner:
docker model run hf.co/openlm-research/open_llama_3b_v2
The prompt style of this model
👍 1
1
#18 opened over 2 years ago
by
Antinis
Upload tokenizer.json
#17 opened over 2 years ago
by
ScottMueller
Adding `safetensors` variant of this model
#16 opened over 2 years ago
by
yongjer
Adding Evaluation Results
#15 opened over 2 years ago
by
leaderboard-pr-bot
model doesn't predict eos token?
#14 opened over 2 years ago
by
joejztang
Upload FlaxLlamaForCausalLM
#13 opened over 2 years ago
by
afmck
delete and do not merge
#12 opened over 2 years ago
by deleted
delete and do not merge
#11 opened over 2 years ago
by deleted
delete and do not merge
#10 opened over 2 years ago
by deleted
delete and do not merge
#9 opened over 2 years ago
by deleted
delete and do not merge
#8 opened over 2 years ago
by deleted
Add tokenizer.json
#7 opened over 2 years ago
by
jonatanklosko
Unexpected `inv_freq` buffers in the checkpoint
2
#6 opened over 2 years ago
by
sadra-barikbin
Add tokenizer.json
#5 opened over 2 years ago
by
bianchidotdev
Adds Flax checkpoint
2
#4 opened almost 3 years ago
by
afmck
I tested the model on mmlu, and the average score is only 26. Is this normal?
#3 opened almost 3 years ago
by
dikw
Adding `safetensors` variant of this model
#1 opened almost 3 years ago
by
SFconvertbot