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

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{
  "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
  }
}