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

e35ef17

verified ·

1 Parent(s): f382c88

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

recipe/scripts/eval_sweep.sh +41 -18

recipe/scripts/eval_sweep.sh CHANGED Viewed

@@ -20,16 +20,25 @@ for model in "$VANILLA_MODEL" "$L2A_MODEL"; do
   [[ -f "$model/config.json" ]] || { echo "Missing model: $model" >&2; exit 2; }
 done
-jobs=()
-for benchmark in "${BENCHMARKS[@]}"; do
-  jobs+=("vanilla|$VANILLA_MODEL|0.5|$benchmark")
-done
-for threshold in "${THRESHOLDS[@]}"; do
-  slug="${threshold/./}"
-  for benchmark in "${BENCHMARKS[@]}"; do
-    jobs+=("token_t${slug}|$L2A_MODEL|$threshold|$benchmark")
-  done
-done
 launch() {
   local gpu="$1" method="$2" model="$3" threshold="$4" benchmark="$5"
@@ -39,21 +48,35 @@ launch() {
   LAST_PID="$!"
 }
-for i in "${!jobs[@]}"; do
-  slot=$((i % ${#GPUS[@]}))
   if [[ -n "${SLOT_PIDS[$slot]:-}" ]]; then
     wait "${SLOT_PIDS[$slot]}"
   fi
-  IFS='|' read -r method model threshold benchmark <<< "${jobs[$i]}"
-  launch "${GPUS[$slot]}" "$method" "$model" "$threshold" "$benchmark"
   SLOT_PIDS[$slot]="$LAST_PID"
 done
-status=0
-for pid in "${SLOT_PIDS[@]}"; do
-  wait "$pid" || status=1
 done
-[[ "$status" == 0 ]] || exit "$status"
 python "$SCRIPT_DIR/summarize_qwen1p7b_router_granularity_20260714.py" \
   --repo "$REPO" \

   [[ -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="$5"
   LAST_PID="$!"
 }
+# 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" \