Instructions to use OpenOneRec/KSA-1.9B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenOneRec/KSA-1.9B-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OpenOneRec/KSA-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-1.9B-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("OpenOneRec/KSA-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-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-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-1.9B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenOneRec/KSA-1.9B-base

SGLang

How to use OpenOneRec/KSA-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-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-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-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-1.9B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenOneRec/KSA-1.9B-base with Docker Model Runner:
```
docker model run hf.co/OpenOneRec/KSA-1.9B-base
```

KSA-1.9B-base / config.json

OpenOneRec

Release KSA-1.9B-base weights and inference runtime

6c91dd5 verified 2 days ago

Raw History Blame Contribute Delete

1.41 kB

{
  "architectures": ["Qwen3ForCausalLM"],
  "auto_map": {
    "AutoModel": "modeling_qwen3.Qwen3Model",
    "AutoModelForCausalLM": "modeling_qwen3.Qwen3ForCausalLM"
  },
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "eos_token_id": 151643,
  "pad_token_id": 151643,
  "dtype": "bfloat16",
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 6144,
  "max_position_embeddings": 131072,
  "max_window_layers": 24,
  "mix_coeff": 0,
  "model_type": "qwen3",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "num_key_value_heads": 16,
  "rms_norm_eps": 1e-6,
  "rope_parameters": {"rope_theta": 1000000, "rope_type": "default"},
  "sliding_window": null,
  "summary_chunk_position_ids_type": "origin",
  "summary_chunk_size": 8,
  "summary_independent_attention_layernorm": false,
  "summary_independent_parameters": true,
  "summary_sliding_chunk_num": [128,128,128,16384,128,128,128,16384,128,128,128,16384,128,128,128,16384,128,128,128,16384,128,128,128,16384],
  "summary_token_begin": 151936,
  "summary_token_num": 1,
  "summary_token_position_ids_type": "last_chunk_slice_right",
  "tie_word_embeddings": false,
  "transformers_version": "5.0.0",
  "truncate_predict_nums": 151936,
  "use_cache": true,
  "use_sliding_window": false,
  "use_summary_attention": true,
  "vocab_size": 151937
}

	{
	"architectures": ["Qwen3ForCausalLM"],
	"auto_map": {
	"AutoModel": "modeling_qwen3.Qwen3Model",
	"AutoModelForCausalLM": "modeling_qwen3.Qwen3ForCausalLM"
	},
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 151643,
	"eos_token_id": 151643,
	"pad_token_id": 151643,
	"dtype": "bfloat16",
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 6144,
	"max_position_embeddings": 131072,
	"max_window_layers": 24,
	"mix_coeff": 0,
	"model_type": "qwen3",
	"num_attention_heads": 16,
	"num_hidden_layers": 24,
	"num_key_value_heads": 16,
	"rms_norm_eps": 1e-6,
	"rope_parameters": {"rope_theta": 1000000, "rope_type": "default"},
	"sliding_window": null,
	"summary_chunk_position_ids_type": "origin",
	"summary_chunk_size": 8,
	"summary_independent_attention_layernorm": false,
	"summary_independent_parameters": true,
	"summary_sliding_chunk_num": [128,128,128,16384,128,128,128,16384,128,128,128,16384,128,128,128,16384,128,128,128,16384,128,128,128,16384],
	"summary_token_begin": 151936,
	"summary_token_num": 1,
	"summary_token_position_ids_type": "last_chunk_slice_right",
	"tie_word_embeddings": false,
	"transformers_version": "5.0.0",
	"truncate_predict_nums": 151936,
	"use_cache": true,
	"use_sliding_window": false,
	"use_summary_attention": true,
	"vocab_size": 151937
	}