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 about 1 month ago

Commit

b09fb2e

verified ·

1 Parent(s): 4711922

Add config.json

Browse files

Files changed (1) hide show

config.json +34 -0

config.json ADDED Viewed

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

+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 576,
+  "initializer_range": 0.041666666666666664,
+  "intermediate_size": 1536,
+  "is_llama_config": true,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 9,
+  "num_hidden_layers": 30,
+  "num_key_value_heads": 3,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_interleaved": false,
+  "rope_parameters": {
+    "rope_theta": 100000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.7.0",
+  "use_cache": true,
+  "vocab_size": 49152
+}