Instructions to use QuantLLM/SmolLM2-135M-QuantLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantLLM/SmolLM2-135M-QuantLLM with Transformers:

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

pipe = pipeline("text-generation", model="QuantLLM/SmolLM2-135M-QuantLLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantLLM/SmolLM2-135M-QuantLLM")
model = AutoModelForCausalLM.from_pretrained("QuantLLM/SmolLM2-135M-QuantLLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuantLLM/SmolLM2-135M-QuantLLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantLLM/SmolLM2-135M-QuantLLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantLLM/SmolLM2-135M-QuantLLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantLLM/SmolLM2-135M-QuantLLM

SGLang

How to use QuantLLM/SmolLM2-135M-QuantLLM 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 "QuantLLM/SmolLM2-135M-QuantLLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantLLM/SmolLM2-135M-QuantLLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "QuantLLM/SmolLM2-135M-QuantLLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantLLM/SmolLM2-135M-QuantLLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use QuantLLM/SmolLM2-135M-QuantLLM with Docker Model Runner:
```
docker model run hf.co/QuantLLM/SmolLM2-135M-QuantLLM
```

codewithdark commited on 18 days ago

Commit

beb9b41

verified ·

1 Parent(s): 69b64b5

Add tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +34 -0

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,34 @@

+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|endoftext|>",
+    "<|im_start|>",
+    "<|im_end|>",
+    "<repo_name>",
+    "<reponame>",
+    "<file_sep>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<jupyter_script>",
+    "<empty_output>"
+  ],
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 8192,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}