Text Generation
Transformers
Safetensors
English
Chinese
qwen3
ksa
ksa-v2
custom_code
long-context
conversational
text-generation-inference
Instructions to use OpenOneRec/KSA-V2-4B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenOneRec/KSA-V2-4B-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenOneRec/KSA-V2-4B-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-V2-4B-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("OpenOneRec/KSA-V2-4B-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-V2-4B-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenOneRec/KSA-V2-4B-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-V2-4B-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpenOneRec/KSA-V2-4B-base
- SGLang
How to use OpenOneRec/KSA-V2-4B-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-V2-4B-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-V2-4B-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-V2-4B-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-V2-4B-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpenOneRec/KSA-V2-4B-base with Docker Model Runner:
docker model run hf.co/OpenOneRec/KSA-V2-4B-base
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a1111f3 | 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 | from contextvars import ContextVar
from functools import wraps
_INDEXER_STEP = ContextVar("indexer_step", default=None)
def shared_indexer_step(forward):
@wraps(forward)
def scoped(*args, **kwargs):
token = _INDEXER_STEP.set({"positions": {}, "bounds": {}})
try:
return forward(*args, **kwargs)
finally:
_INDEXER_STEP.reset(token)
return scoped
def shared_indexer_positions(source, kind, factory, *layout):
scope = _INDEXER_STEP.get()
if scope is None:
return factory()
key = (id(source), kind, *layout)
if key not in scope["positions"]:
# Retain the source, so tensor identity cannot be recycled in this step.
scope["positions"][key] = (source, factory())
return scope["positions"][key][1]
def _validated_position_bounds(position_ids):
scope = _INDEXER_STEP.get()
key = id(position_ids)
if scope is not None and key in scope["bounds"]:
return scope["bounds"][key][1]
low = int(position_ids.min())
if low < 0:
raise ValueError("indexer position IDs must be non-negative")
bounds = (low, int(position_ids.max()))
if scope is not None:
scope["bounds"][key] = (position_ids, bounds)
return bounds
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