Text Generation
Transformers
Safetensors
English
qwen3
long-context
sparse-attention
aha
l2a-style
reproducibility
conversational
text-generation-inference
Instructions to use keepsloading/icml_repro_scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keepsloading/icml_repro_scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="keepsloading/icml_repro_scratch") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("keepsloading/icml_repro_scratch") model = AutoModelForCausalLM.from_pretrained("keepsloading/icml_repro_scratch", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use keepsloading/icml_repro_scratch with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "keepsloading/icml_repro_scratch" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "keepsloading/icml_repro_scratch", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/keepsloading/icml_repro_scratch
- SGLang
How to use keepsloading/icml_repro_scratch 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 "keepsloading/icml_repro_scratch" \ --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": "keepsloading/icml_repro_scratch", "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 "keepsloading/icml_repro_scratch" \ --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": "keepsloading/icml_repro_scratch", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use keepsloading/icml_repro_scratch with Docker Model Runner:
docker model run hf.co/keepsloading/icml_repro_scratch
| { | |
| "schema": "aha-l2a-qwen3-repro-v1", | |
| "source_commit": "b47a549", | |
| "files": { | |
| "../model.safetensors": "22c971a65f7f1835b0e2a38b45f2f92191f5d8b63b30969f2f61a69d30afbc05", | |
| "data/am_distilled_long_mix/train/data-00000-of-00001.arrow": "27f158ba22512269d33e8fb14db97a84e541ca9b701fba73a98e70c73687f45c", | |
| "data/eval_inputs/helmet_icl_8k_n50_per_config.jsonl": "76e4c8115014d11e2dbf47ca8a2e300c490fd8e454326d5e589b12682b7f140e", | |
| "data/eval_inputs/mrcr_8k_2_4_8needle_n10_per_config.jsonl": "7074281289fa051278e213ecea523cedf3f609e89b6ae13ab575c075f386ddc4", | |
| "modeling_aha_qwen3.py": "4b10c633ab62dbbacbd2a9ac0c69f8225876e97f70bd6ddc4b37367053e2fc1b", | |
| "dynamic_duo_train.py": "14188f72ae418831ccda997d0e4cae71c808a59b96aabd3be1e289536a1e073e", | |
| "sft.py": "d1f7ae5818639b25ed130433073f4717dcad310dc941bf4a13a9063730b4e555" | |
| } | |
| } | |