Text Generation
Transformers
Safetensors
English
Chinese
qwen3
ksa
ksa-v2
custom_code
long-context
conversational
text-generation-inference
Instructions to use OpenOneRec/KSA-V2-1.9B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenOneRec/KSA-V2-1.9B-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenOneRec/KSA-V2-1.9B-base", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpenOneRec/KSA-V2-1.9B-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OpenOneRec/KSA-V2-1.9B-base", trust_remote_code=True, device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenOneRec/KSA-V2-1.9B-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenOneRec/KSA-V2-1.9B-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenOneRec/KSA-V2-1.9B-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenOneRec/KSA-V2-1.9B-base
- SGLang
How to use OpenOneRec/KSA-V2-1.9B-base 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 "OpenOneRec/KSA-V2-1.9B-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenOneRec/KSA-V2-1.9B-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "OpenOneRec/KSA-V2-1.9B-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenOneRec/KSA-V2-1.9B-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenOneRec/KSA-V2-1.9B-base with Docker Model Runner:
docker model run hf.co/OpenOneRec/KSA-V2-1.9B-base
File size: 2,518 Bytes
03c3dff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | #!/usr/bin/env python3
"""Generate text with a local KSA V2 export and its bundled kernels."""
import argparse
from pathlib import Path
import sys
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Interactively generate text with a local model.")
parser.add_argument("--model", type=Path, default=Path(__file__).resolve().parent)
parser.add_argument("--prompt", help="Omit for interactive text completion.")
parser.add_argument("--max-new-tokens", type=int, default=256)
parser.add_argument("--apply-chat-template", action="store_true")
return parser.parse_args()
def main() -> None:
args = parse_args()
model_dir = args.model.resolve()
# Select the export's kernels before importing either runtime or Transformers.
for directory in (model_dir / "kernel/deps", model_dir / "kernel"):
sys.path.insert(0, str(directory))
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_dir, local_files_only=True)
model = AutoModelForCausalLM.from_pretrained(
model_dir,
trust_remote_code=True,
local_files_only=True,
dtype=torch.bfloat16,
device_map={"": "cuda:0"},
attn_implementation="sdpa",
).eval()
def complete(prompt: str) -> str:
if args.apply_chat_template:
prompt = tokenizer.apply_chat_template(
[{"role": "user", "content": prompt}],
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
output_ids = model.generate(
**inputs,
max_new_tokens=args.max_new_tokens,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
)
new_tokens = output_ids[0, inputs["input_ids"].shape[1] :]
return tokenizer.decode(new_tokens, skip_special_tokens=True)
if args.prompt is not None:
print(complete(args.prompt))
return
print("Enter a prompt, or type 'exit' to stop.")
while True:
try:
prompt = input("Prompt: ").strip()
except (EOFError, KeyboardInterrupt):
print()
break
if prompt.lower() in {"exit", "quit"}:
break
if prompt:
print(complete(prompt))
if __name__ == "__main__":
main()
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