Instructions to use keepsloading/icml_repro_scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

icml_repro_scratch / recipe /scripts /reproduce.sh

keepsloading

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	#!/usr/bin/env bash
	set -euo pipefail

	SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
	REPO="$(cd "$SCRIPT_DIR/../.." && pwd)"
	IMAGE="${IMAGE:-aha-l2a-qwen3-repro:torch2.9.1-cu128}"
	GPU_COUNT="$(nvidia-smi -L \| wc -l)"
	if [[ "$GPU_COUNT" -lt 1 ]]; then
	echo "At least one NVIDIA GPU is required." >&2
	exit 2
	fi
	GPU_AHA="${GPU_AHA:-0}"
	if [[ -z "${GPU_L2A+x}" ]]; then
	GPU_L2A=0
	if [[ "$GPU_COUNT" -ge 2 ]]; then
	GPU_L2A=1
	fi
	fi

	for gpu in "$GPU_AHA" "$GPU_L2A"; do
	if [[ ! "$gpu" =~ ^[0-9]+$ ]] \|\| (( gpu >= GPU_COUNT )); then
	echo "Invalid GPU index $gpu; nvidia-smi reports $GPU_COUNT visible GPU(s)." >&2
	exit 2
	fi
	done

	echo "Training mode: one GPU per experimental variant (no DDP/FSDP/DeepSpeed)."
	echo "AHA variant GPU: $GPU_AHA"
	echo "L2A-style variant GPU: $GPU_L2A"
	if [[ "$GPU_AHA" == "$GPU_L2A" ]]; then
	echo "The two variants will run sequentially on the same GPU."
	else
	echo "The two variants will run concurrently on two independent GPUs."
	fi
	if [[ "$GPU_COUNT" -gt 2 ]]; then
	echo "Visible GPUs: $GPU_COUNT; this recipe intentionally does not use all GPUs."
	fi

	docker build -t "$IMAGE" -f "$REPO/recipe/Dockerfile" "$REPO/recipe"
	docker run --rm --gpus all --ipc=host \
	--ulimit memlock=-1 --ulimit stack=67108864 \
	-e GPU_AHA="$GPU_AHA" -e GPU_L2A="$GPU_L2A" \
	-v "$REPO:/workspace" \
	-w /workspace/recipe \
	"$IMAGE" bash scripts/train_both.sh