narendra0892/crc-ai-csv
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How to use narendra0892/autotrain-p7nex-vq4kc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="narendra0892/autotrain-p7nex-vq4kc") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("narendra0892/autotrain-p7nex-vq4kc")
model = AutoModelForCausalLM.from_pretrained("narendra0892/autotrain-p7nex-vq4kc", device_map="auto")How to use narendra0892/autotrain-p7nex-vq4kc with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "narendra0892/autotrain-p7nex-vq4kc"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "narendra0892/autotrain-p7nex-vq4kc",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/narendra0892/autotrain-p7nex-vq4kc
How to use narendra0892/autotrain-p7nex-vq4kc with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "narendra0892/autotrain-p7nex-vq4kc" \
--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": "narendra0892/autotrain-p7nex-vq4kc",
"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 "narendra0892/autotrain-p7nex-vq4kc" \
--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": "narendra0892/autotrain-p7nex-vq4kc",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use narendra0892/autotrain-p7nex-vq4kc with Docker Model Runner:
docker model run hf.co/narendra0892/autotrain-p7nex-vq4kc
No validation metrics available
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the Hugging Face Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'search_query: autotrain',
'search_query: auto train',
'search_query: i love autotrain',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Base model
meta-llama/Llama-4-Scout-17B-16E