Text Generation
Transformers
Safetensors
English
qwen3_5_text
qwen3.5
tinyllama
riscv
int4
in-weights
compiled-weights
exact-arithmetic
qwenti
conversational
Instructions to use OpceanAI/qwenti-inference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpceanAI/qwenti-inference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpceanAI/qwenti-inference") 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("OpceanAI/qwenti-inference") model = AutoModelForCausalLM.from_pretrained("OpceanAI/qwenti-inference", 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 OpceanAI/qwenti-inference with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpceanAI/qwenti-inference" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpceanAI/qwenti-inference", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OpceanAI/qwenti-inference
- SGLang
How to use OpceanAI/qwenti-inference 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 "OpceanAI/qwenti-inference" \ --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": "OpceanAI/qwenti-inference", "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 "OpceanAI/qwenti-inference" \ --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": "OpceanAI/qwenti-inference", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OpceanAI/qwenti-inference with Docker Model Runner:
docker model run hf.co/OpceanAI/qwenti-inference
Commit History
2 days ago
-
Paper PDF regenerated
Verified signatured2c6a3d -
Paper: antislop pass on the abstract and section 11
Verified signatureae901c0 -
Card: rewritten, credits Qwenputer, figures restored
Verified signature5f77090 -
Paper PDF with section 11 and the chip evidence
Verified signature3223f65 -
Paper: section 11 with the mu gate and the chip evidence
Verified signature921be33 -
Card: mu-gated readout verified, chip 17/24, text 6/6
Verified signature4b1a2bf -
Checkpoint with the mu-gated readout: text faithful, chip emits when driven
Verified signatureeaa2651 -
Paper PDF regenerated
Verified signaturecb276b3 -
Paper: section 11 rewritten with the fix and the verified measurements
Verified signature062237c -
Card: verified against the base, 6/6 first tokens; chip readout gated off
Verified signature53aba07 -
Working checkpoint: text readout fixed (chip readout gated off)
Verified signature5ffe32c -
Delete files logs/ with huggingface_hub
Verified signature41e8502 -
Model card: drop the logs entry
Verified signature9eefd53 -
Paper as a print-quality PDF (10 pages), corrected figures 5 and 8, PDF builder
Verified signature5b8649d -
Tokenizer config: drop the 263k chip entries that made the file 46 MB and triggered the parsing warning
Verified signature29eafb6 -
Paper: section 11, post-publication check of the merged checkpoint
Verified signature2127609 -
Log of the published-weights verification
Verified signatureeed3ed7 -
Model card: status warning, the published checkpoint has a degenerate head
Verified signature3ed1288 -
Model card: inline figures and a usage section
Verified signaturec51e423 -
Merged checkpoint: Qwen3.5-4B + 36 circuit layers with TinyLLama-v0 in the weight memory
Verified signature3e86f6d -
Model card, paper, figures, logs and scripts
Verified signature2049fc8 -
initial commit
Verified signature97cc8e0