Instructions to use HuggingFaceTB/SmolLM2-1.7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct")
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]:]))

Transformers.js

How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('text-generation', 'HuggingFaceTB/SmolLM2-1.7B-Instruct');

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolLM2-1.7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceTB/SmolLM2-1.7B-Instruct

SGLang

How to use HuggingFaceTB/SmolLM2-1.7B-Instruct 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 "HuggingFaceTB/SmolLM2-1.7B-Instruct" \
    --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": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
		"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 "HuggingFaceTB/SmolLM2-1.7B-Instruct" \
        --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": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HuggingFaceTB/SmolLM2-1.7B-Instruct with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolLM2-1.7B-Instruct
```

Xenova HF Staff commited on Mar 6, 2025

Commit

57aa3c6

verified ·

1 Parent(s): 4db41b7

Transformers.js improvements (#25)

Browse files

- [WIP] Transformers.js improvements (b86e3eda672bc29427317d7d6753fbf808730d94)
- Update README.md (26cd37002254a59f114c509de0b80ef80aaf90d6)

Files changed (2) hide show

README.md +29 -2
config.json +5 -0

README.md CHANGED Viewed

@@ -40,7 +40,7 @@ For more details refer to: https://github.com/huggingface/smollm. You will find
 ### How to use
-### Transformers
 ```bash
 pip install transformers
 ```
@@ -62,13 +62,40 @@ print(tokenizer.decode(outputs[0]))
 ```
-### Chat in TRL
 You can also use the TRL CLI to chat with the model from the terminal:
 ```bash
 pip install trl
 trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu
 ```
 ## Evaluation
 In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them.

 ### How to use
+#### Transformers
 ```bash
 pip install transformers
 ```
 ```
+#### Chat in TRL
 You can also use the TRL CLI to chat with the model from the terminal:
 ```bash
 pip install trl
 trl chat --model_name_or_path HuggingFaceTB/SmolLM2-1.7B-Instruct --device cpu
 ```
+#### Transformers.js
+```bash
+npm i @huggingface/transformers
+```
+```js
+import { pipeline } from "@huggingface/transformers";
+// Create a text generation pipeline
+const generator = await pipeline(
+  "text-generation",
+  "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+);
+// Define the list of messages
+const messages = [
+  { role: "system", content: "You are a helpful assistant." },
+  { role: "user", content: "Tell me a joke." },
+];
+// Generate a response
+const output = await generator(messages, { max_new_tokens: 128 });
+console.log(output[0].generated_text.at(-1).content);
+// "Why don't scientists trust atoms?\n\nBecause they make up everything!"
+```
 ## Evaluation
 In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them.

config.json CHANGED Viewed

@@ -25,9 +25,14 @@
   "torch_dtype": "bfloat16",
   "transformers_version": "4.42.3",
   "transformers.js_config": {
     "kv_cache_dtype": {
       "q4f16": "float16",
       "fp16": "float16"
     }
   },
   "use_cache": true,

   "torch_dtype": "bfloat16",
   "transformers_version": "4.42.3",
   "transformers.js_config": {
+    "dtype": "q4",
     "kv_cache_dtype": {
       "q4f16": "float16",
       "fp16": "float16"
+    },
+    "use_external_data_format": {
+      "model.onnx": true,
+      "model_fp16.onnx": true
     }
   },
   "use_cache": true,