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
| set -euo pipefail | |
| SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| RECIPE="$(cd "$SCRIPT_DIR/.." && pwd)" | |
| GPU_AHA="${GPU_AHA:-0}" | |
| GPU_L2A="${GPU_L2A:-1}" | |
| cd "$RECIPE" | |
| echo "AHA = per-token, per-KV-head gate; assigned visible GPU $GPU_AHA." | |
| echo "L2A-style = per-token, head-shared gate; assigned visible GPU $GPU_L2A." | |
| echo "Each variant is a separate world-size-1 run with global batch size 1." | |
| python "$SCRIPT_DIR/validate_release.py" | |
| python -m unittest -v tests.test_router_granularity | |
| if [[ "$GPU_AHA" == "$GPU_L2A" ]]; then | |
| "$SCRIPT_DIR/train_one.sh" aha "$GPU_AHA" | |
| "$SCRIPT_DIR/train_one.sh" l2a_style "$GPU_L2A" | |
| else | |
| "$SCRIPT_DIR/train_one.sh" aha "$GPU_AHA" & aha_pid=$! | |
| "$SCRIPT_DIR/train_one.sh" l2a_style "$GPU_L2A" & l2a_pid=$! | |
| status=0 | |
| wait "$aha_pid" || status=1 | |
| wait "$l2a_pid" || status=1 | |
| [[ "$status" == 0 ]] || exit "$status" | |
| fi | |
| echo "AHA selected checkpoint: ${OUTPUT_ROOT:-$RECIPE/../outputs}/aha/stage2/checkpoint-25" | |
| echo "L2A-style selected checkpoint: ${OUTPUT_ROOT:-$RECIPE/../outputs}/l2a_style/stage2/checkpoint-25" | |