nlpai-lab/kullm-v2
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How to use realPCH/kullm-v2-1epoch with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="realPCH/kullm-v2-1epoch") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("realPCH/kullm-v2-1epoch")
model = AutoModelForCausalLM.from_pretrained("realPCH/kullm-v2-1epoch")How to use realPCH/kullm-v2-1epoch with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "realPCH/kullm-v2-1epoch"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "realPCH/kullm-v2-1epoch",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/realPCH/kullm-v2-1epoch
How to use realPCH/kullm-v2-1epoch with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "realPCH/kullm-v2-1epoch" \
--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": "realPCH/kullm-v2-1epoch",
"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 "realPCH/kullm-v2-1epoch" \
--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": "realPCH/kullm-v2-1epoch",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use realPCH/kullm-v2-1epoch with Docker Model Runner:
docker model run hf.co/realPCH/kullm-v2-1epoch
We fine-tuned this model based on yanolja/KoSOLAR-10.7B-v0.1
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "realPCH/ko_solra_merge"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "[INST] Put instruction here. [/INST]"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))