Instructions to use Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill")
model = AutoModelForCausalLM.from_pretrained("Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill", 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 Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill

SGLang

How to use Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill 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 "Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill" \
    --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": "Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill",
		"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 "Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill" \
        --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": "Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill with Docker Model Runner:
```
docker model run hf.co/Sunnylululu/Qwen2.5-1.5B-Open-R1-Distill
```

Sunnylululu commited on Mar 3, 2025

Commit

cbd622e

verified ·

1 Parent(s): e5f896c

Model save

Browse files

Files changed (6) hide show

README.md +2 -4
all_results.json +4 -4
config.json +1 -1
train_results.json +4 -4
trainer_state.json +4 -4
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -1,10 +1,8 @@
 ---
-datasets: open-r1/OpenR1-Math-220k
 library_name: transformers
 model_name: Qwen2.5-1.5B-Open-R1-Distill
 tags:
 - generated_from_trainer
-- open-r1
 - trl
 - sft
 licence: license
@@ -12,7 +10,7 @@ licence: license
 # Model Card for Qwen2.5-1.5B-Open-R1-Distill
-This model is a fine-tuned version of [None](https://huggingface.co/None) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset.
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
@@ -28,7 +26,7 @@ print(output["generated_text"])
 ## Training procedure
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/648848014/huggingface/runs/94h4qzbt)
 This model was trained with SFT.

 ---
 library_name: transformers
 model_name: Qwen2.5-1.5B-Open-R1-Distill
 tags:
 - generated_from_trainer
 - trl
 - sft
 licence: license
 # Model Card for Qwen2.5-1.5B-Open-R1-Distill
+This model is a fine-tuned version of [None](https://huggingface.co/None).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
 ## Training procedure
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/648848014/huggingface/runs/3n5lktnf)
 This model was trained with SFT.

all_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "total_flos": 2.5375909456379904e+17,
-    "train_loss": 0.8519784138752864,
-    "train_runtime": 3243.3529,
     "train_samples": 3472,
-    "train_samples_per_second": 0.607,
-    "train_steps_per_second": 0.04
 }

 {
     "total_flos": 2.5375909456379904e+17,
+    "train_loss": 0.0,
+    "train_runtime": 2.0428,
     "train_samples": 3472,
+    "train_samples_per_second": 964.355,
+    "train_steps_per_second": 63.638
 }

config.json CHANGED Viewed

@@ -23,7 +23,7 @@
   "tie_word_embeddings": true,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.50.0.dev0",
-  "use_cache": true,
   "use_sliding_window": false,
   "vocab_size": 151936
 }

   "tie_word_embeddings": true,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.50.0.dev0",
+  "use_cache": false,
   "use_sliding_window": false,
   "vocab_size": 151936
 }

train_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "total_flos": 2.5375909456379904e+17,
-    "train_loss": 0.8519784138752864,
-    "train_runtime": 3243.3529,
     "train_samples": 3472,
-    "train_samples_per_second": 0.607,
-    "train_steps_per_second": 0.04
 }

 {
     "total_flos": 2.5375909456379904e+17,
+    "train_loss": 0.0,
+    "train_runtime": 2.0428,
     "train_samples": 3472,
+    "train_samples_per_second": 964.355,
+    "train_steps_per_second": 63.638
 }

trainer_state.json CHANGED Viewed

@@ -194,10 +194,10 @@
       "epoch": 10.0,
       "step": 130,
       "total_flos": 2.5375909456379904e+17,
-      "train_loss": 0.8519784138752864,
-      "train_runtime": 3243.3529,
-      "train_samples_per_second": 0.607,
-      "train_steps_per_second": 0.04
     }
   ],
   "logging_steps": 5,

       "epoch": 10.0,
       "step": 130,
       "total_flos": 2.5375909456379904e+17,
+      "train_loss": 0.0,
+      "train_runtime": 2.0428,
+      "train_samples_per_second": 964.355,
+      "train_steps_per_second": 63.638
     }
   ],
   "logging_steps": 5,

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
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