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
File size: 2,680 Bytes
46b9eea f382c88 46b9eea 5c2f3ce 46b9eea e35ef17 46b9eea 6b0b5b4 46b9eea e35ef17 46b9eea e35ef17 46b9eea e35ef17 46b9eea e35ef17 46b9eea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | #!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
RECIPE="$(cd "$SCRIPT_DIR/.." && pwd)"
REPO="$(cd "$RECIPE/.." && pwd)"
OUTPUT_ROOT="${OUTPUT_ROOT:-$REPO/outputs}"
EVAL_ROOT="${EVAL_ROOT:-$OUTPUT_ROOT/eval}"
VANILLA_MODEL="${VANILLA_DIR:-$REPO}"
AHA_MODEL="${AHA_MODEL:-$OUTPUT_ROOT/aha/stage2/checkpoint-25}"
L2A_MODEL="${L2A_MODEL:-$OUTPUT_ROOT/l2a_style/stage2/checkpoint-25}"
GPU_LIST="${GPU_LIST:-0}"
THRESHOLDS=(0.45 0.525 0.575 0.65)
BENCHMARKS=(ruler_a ruler_b babilong helmet mrcr)
IFS=',' read -r -a GPUS <<< "$GPU_LIST"
declare -A SLOT_PIDS=()
mkdir -p "$EVAL_ROOT"
for model in "$VANILLA_MODEL" "$L2A_MODEL"; do
[[ -f "$model/config.json" ]] || { echo "Missing model: $model" >&2; exit 2; }
done
# Upload outputs to HF Hub after each config group to survive timeouts
upload_to_hub() {
echo "=== Incremental upload to HF Hub ==="
python -c "
from huggingface_hub import HfApi
import os
api = HfApi()
if os.path.exists('/workspace/outputs'):
api.upload_folder(
folder_path='/workspace/outputs',
repo_id='keepsloading/icml_repro_scratch',
repo_type='model',
path_in_repo='outputs'
)
print('Incremental upload complete.')
else:
print('No outputs dir yet.')
" || echo "Upload failed (non-fatal), continuing..."
}
launch() {
local gpu="$1" method="$2" model="$3" threshold="$4" benchmark="$5"
EVAL_ROOT="$EVAL_ROOT" RULER_LIMIT=20 BABILONG_LIMIT=50 \
AHA_GATE_HARD_THRESHOLD="$threshold" \
bash -x "$SCRIPT_DIR/eval_cell.sh" "$method" "$model" "$gpu" "$benchmark" &
LAST_PID="$!"
}
# Run vanilla baseline
for benchmark in "${BENCHMARKS[@]}"; do
slot=0
if [[ -n "${SLOT_PIDS[$slot]:-}" ]]; then
wait "${SLOT_PIDS[$slot]}"
fi
launch "${GPUS[$slot]}" "vanilla" "$VANILLA_MODEL" "0.5" "$benchmark"
SLOT_PIDS[$slot]="$LAST_PID"
done
for pid in "${SLOT_PIDS[@]:-}"; do wait "$pid" || true; done
SLOT_PIDS=()
upload_to_hub
# Run each L2A threshold group
for threshold in "${THRESHOLDS[@]}"; do
slug="${threshold/./}"
method="token_t${slug}"
for benchmark in "${BENCHMARKS[@]}"; do
slot=0
if [[ -n "${SLOT_PIDS[$slot]:-}" ]]; then
wait "${SLOT_PIDS[$slot]}"
fi
launch "${GPUS[$slot]}" "$method" "$L2A_MODEL" "$threshold" "$benchmark"
SLOT_PIDS[$slot]="$LAST_PID"
done
for pid in "${SLOT_PIDS[@]:-}"; do wait "$pid" || true; done
SLOT_PIDS=()
upload_to_hub
done
python "$SCRIPT_DIR/summarize_qwen1p7b_router_granularity_20260714.py" \
--repo "$REPO" \
--eval-root "$EVAL_ROOT" \
--baseline-root "$EVAL_ROOT/vanilla" \
--input-dir "$RECIPE/data/eval_inputs" \
--output-dir "$OUTPUT_ROOT/summary"
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