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
Download config.json from OpenOneRec/KSA-V2-1.9B-base: direct link, hf CLI and curl.
- Browser
- Download file 2.47 kB
-
https://huggingface.co/OpenOneRec/KSA-V2-1.9B-base/resolve/main/config.json
- Command line
-
hf download hf://OpenOneRec/KSA-V2-1.9B-base/config.json
-
curl -L -o config.json https://huggingface.co/OpenOneRec/KSA-V2-1.9B-base/resolve/main/config.json
2.47 kB
| { | |
| "architectures": [ | |
| "Qwen3ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoModel": "modeling_qwen3.Qwen3Model", | |
| "AutoModelForCausalLM": "modeling_qwen3.Qwen3ForCausalLM" | |
| }, | |
| "bos_token_id": 151643, | |
| "eos_token_id": 151643, | |
| "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": 0.0, | |
| "model_type": "qwen3", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 16, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000, | |
| "sksa_apply_sparse_attention": true, | |
| "sksa_enabled": true, | |
| "sksa_indexer_block_size": 4, | |
| "sksa_indexer_chunk_attention_include_summary": false, | |
| "sksa_indexer_chunk_attention_qk_source": "independent", | |
| "sksa_indexer_chunk_attention_v_source": "independent", | |
| "sksa_indexer_force_sink": true, | |
| "sksa_indexer_head_dim": 128, | |
| "sksa_indexer_heads": 8, | |
| "sksa_indexer_qk_norm": false, | |
| "sksa_indexer_query_block_size": 512, | |
| "sksa_indexer_query_rank": 512, | |
| "sksa_indexer_rope_dim": 64, | |
| "sksa_indexer_scorer_backend": "fused", | |
| "sksa_indexer_summary_key_source": "chunk_attention", | |
| "sksa_indexer_teacher_pooling": "max", | |
| "sksa_indexer_token_budget": 4096, | |
| "sksa_local_window_tokens": 1024, | |
| "sksa_routing_mode": "summary_indexer", | |
| "sksa_sparse_full_layers": [ | |
| 3, | |
| 7, | |
| 11, | |
| 15, | |
| 19, | |
| 23 | |
| ], | |
| "sliding_window": null, | |
| "summary_chunk_position_ids_type": "origin", | |
| "summary_chunk_size": 8, | |
| "summary_independent_attention_layernorm": false, | |
| "summary_independent_parameters": true, | |
| "summary_layer_freq": "([1]*4)*6", | |
| "summary_meta_pool_block_size": 512, | |
| "summary_meta_pool_group_size": "([128]*1+[128]*1+[128]*1+[0]*1)*6", | |
| "summary_meta_pool_mass_correction": "log_group_size", | |
| "summary_meta_pool_mode": "dimension_wise", | |
| "summary_meta_pool_position": "group_last", | |
| "summary_partial_chunk_policy": "defer", | |
| "summary_sliding_chunk_num": "([128]*3+[16384]*1)*6", | |
| "summary_token_begin": 151936, | |
| "summary_token_num": 1, | |
| "summary_token_position_ids_type": "last_chunk_slice_right", | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "5.0.0", | |
| "truncate_predict_nums": 151936, | |
| "use_cache": true, | |
| "use_sliding_window": false, | |
| "use_summary_attention": true, | |
| "vocab_size": 151937 | |
| } | |