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
| { | |
| "tokenizer_path": "/data/sjm/AHA/models/Qwen3-1.7B", | |
| "thinking": false, | |
| "truncation": false, | |
| "target_context_k": 8, | |
| "helmet": { | |
| "path": "/data/sjm/AHA/AHA-Qwen3/experiments/qwen3_1p7b_fourbench_8k_broad_20260713/inputs/helmet_icl_8k_n50_per_config.jsonl", | |
| "source": "/data/sjm/AHA/AHA-Qwen3/experiments/qwen3_1p7b_fivebench_n100_20260712/helmet8k_rows_n100.jsonl", | |
| "configs": [ | |
| "trec_coarse", | |
| "trec_fine", | |
| "banking77", | |
| "clinic150", | |
| "nlu" | |
| ], | |
| "samples_per_config": 50, | |
| "total_samples": 250, | |
| "min_prompt_tokens": 6344, | |
| "max_prompt_tokens": 7413 | |
| }, | |
| "mrcr": { | |
| "path": "/data/sjm/AHA/AHA-Qwen3/experiments/qwen3_1p7b_fourbench_8k_broad_20260713/inputs/mrcr_8k_2_4_8needle_n10_per_config.jsonl", | |
| "source": "/data/sjm/AHA/AHA-Qwen3/experiments/qwen3_1p7b_fourbench_broad_20260713/inputs/mrcr_8k_16k_2_4_8needle_n10_per_config.jsonl", | |
| "configs": [ | |
| "8k_2needle", | |
| "8k_4needle", | |
| "8k_8needle" | |
| ], | |
| "samples_per_config": 10, | |
| "total_samples": 30, | |
| "min_prompt_tokens": 4312, | |
| "max_prompt_tokens": 8028 | |
| } | |
| } | |