microsoft/orca-math-word-problems-200k
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How to use Abigail45/Green with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Abigail45/Green") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Abigail45/Green", dtype="auto")How to use Abigail45/Green with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abigail45/Green"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Abigail45/Green",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Abigail45/Green
How to use Abigail45/Green with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Abigail45/Green" \
--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": "Abigail45/Green",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Abigail45/Green" \
--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": "Abigail45/Green",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Abigail45/Green with Docker Model Runner:
docker model run hf.co/Abigail45/Green
Green is an open-source long-context model based on Mistral.
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "Abigail45/Green"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
prompt = "Write a short poem about green forests."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(output[0], skip_special_tokens=True))
Base model
mistralai/Mistral-7B-v0.3
docker model run hf.co/Abigail45/Green