Instructions to use mlfoundations-dev/e1_science_longest_r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlfoundations-dev/e1_science_longest_r1 with Transformers:

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

pipe = pipeline("text-generation", model="mlfoundations-dev/e1_science_longest_r1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlfoundations-dev/e1_science_longest_r1")
model = AutoModelForCausalLM.from_pretrained("mlfoundations-dev/e1_science_longest_r1")
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

vLLM

How to use mlfoundations-dev/e1_science_longest_r1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlfoundations-dev/e1_science_longest_r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlfoundations-dev/e1_science_longest_r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlfoundations-dev/e1_science_longest_r1

SGLang

How to use mlfoundations-dev/e1_science_longest_r1 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 "mlfoundations-dev/e1_science_longest_r1" \
    --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": "mlfoundations-dev/e1_science_longest_r1",
		"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 "mlfoundations-dev/e1_science_longest_r1" \
        --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": "mlfoundations-dev/e1_science_longest_r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlfoundations-dev/e1_science_longest_r1 with Docker Model Runner:
```
docker model run hf.co/mlfoundations-dev/e1_science_longest_r1
```

ryanmarten commited on May 13, 2025

Commit

0d818e6

verified ·

1 Parent(s): 78dbd1e

End of training

Browse files

Files changed (5) hide show

README.md +2 -1
all_results.json +4 -4
train_results.json +4 -4
trainer_state.json +0 -0
training_loss.png +0 -0

README.md CHANGED Viewed

@@ -4,6 +4,7 @@ license: apache-2.0
 base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - llama-factory
 - generated_from_trainer
 model-index:
 - name: e1_science_longest_r1
@@ -15,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 # e1_science_longest_r1
-This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on an unknown dataset.
 ## Model description

 base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
 - llama-factory
+- full
 - generated_from_trainer
 model-index:
 - name: e1_science_longest_r1
 # e1_science_longest_r1
+This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_science_longest_r1 dataset.
 ## Model description

all_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 4.9869587897756915,
     "total_flos": 2.8011398523411825e+18,
-    "train_loss": 0.0,
-    "train_runtime": 4.4131,
-    "train_samples_per_second": 34739.752,
-    "train_steps_per_second": 270.785
 }

 {
     "epoch": 4.9869587897756915,
     "total_flos": 2.8011398523411825e+18,
+    "train_loss": 0.4013182716763668,
+    "train_runtime": 47374.7157,
+    "train_samples_per_second": 3.236,
+    "train_steps_per_second": 0.025
 }

train_results.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
     "epoch": 4.9869587897756915,
     "total_flos": 2.8011398523411825e+18,
-    "train_loss": 0.0,
-    "train_runtime": 4.4131,
-    "train_samples_per_second": 34739.752,
-    "train_steps_per_second": 270.785
 }

 {
     "epoch": 4.9869587897756915,
     "total_flos": 2.8011398523411825e+18,
+    "train_loss": 0.4013182716763668,
+    "train_runtime": 47374.7157,
+    "train_samples_per_second": 3.236,
+    "train_steps_per_second": 0.025
 }

trainer_state.json CHANGED Viewed

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training_loss.png CHANGED Viewed