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)

# pip install -U transformers accelerate
# 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=256)
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
```

keepsloading commited on Jul 18

Commit

4a92d99

verified ·

1 Parent(s): 32eaeae

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

.gitattributes +0 -3
recipe/run_reproduction.sh +11 -0

.gitattributes CHANGED Viewed

@@ -2,6 +2,3 @@
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.jsonl filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
-outputs/l2a_style/stage2/checkpoint-25/optimizer.pt filter=lfs diff=lfs merge=lfs -text
-outputs/l2a_style/stage2/checkpoint-50/optimizer.pt filter=lfs diff=lfs merge=lfs -text
-outputs/l2a_style/stage2/checkpoint-75/optimizer.pt filter=lfs diff=lfs merge=lfs -text

 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.jsonl filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text

recipe/run_reproduction.sh CHANGED Viewed

@@ -24,6 +24,17 @@ if 'dtype=get_dtype(dtype),' in content:
     print('lm_eval patched successfully!')
 else:
     print('lm_eval already patched or target not found.')
 "
 echo "Downloading NLTK requirements..."

     print('lm_eval patched successfully!')
 else:
     print('lm_eval already patched or target not found.')
+qa_py = Path(lm_eval.__file__).parent / 'tasks' / 'ruler' / 'qa_utils.py'
+content = qa_py.read_text(encoding='utf-8')
+old_url = 'http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_distractor_v1.json'
+new_url = 'https://huggingface.co/datasets/namlh2004/hotpotqa/resolve/main/hotpot_dev_distractor_v1.json'
+if old_url in content:
+    patched = content.replace(old_url, new_url)
+    qa_py.write_text(patched, encoding='utf-8')
+    print('qa_utils patched successfully!')
+else:
+    print('qa_utils already patched or target not found.')
 "
 echo "Downloading NLTK requirements..."