Instructions to use PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0 with Transformers:

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

pipe = pipeline("text-generation", model="PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0")
model = AutoModelForCausalLM.from_pretrained("PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0

SGLang

How to use PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0 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 "PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0" \
    --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": "PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0",
		"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 "PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0" \
        --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": "PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0 with Docker Model Runner:
```
docker model run hf.co/PruningVSQuantization/SmolLM2-1.7B-Instruct-gptq-bits4-seed0
```

S1mKu commited on May 10, 2025

Commit

cf0d323

verified ·

1 Parent(s): 3c10506

Upload LlamaForCausalLM

Browse files

Files changed (3) hide show

config.json +73 -0
generation_config.json +7 -0
model.safetensors +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,73 @@

+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 32,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "quantization_config": {
+    "backend": "auto",
+    "batch_size": 1,
+    "bits": 4,
+    "block_name_to_quantize": null,
+    "cache_block_outputs": true,
+    "checkpoint_format": "gptq_v2",
+    "damp_percent": 0.1,
+    "dataset": "c4",
+    "desc_act": false,
+    "exllama_config": {
+      "version": 1
+    },
+    "group_size": 128,
+    "max_input_length": null,
+    "meta": {
+      "quantizer": [
+        "optimum:1.24.0",
+        "gptqmodel:2.2.0"
+      ]
+    },
+    "model_seqlen": null,
+    "module_name_preceding_first_block": null,
+    "modules_in_block_to_quantize": null,
+    "pad_token_id": null,
+    "quant_method": "gptq",
+    "sym": true,
+    "tokenizer": null,
+    "true_sequential": true,
+    "use_cuda_fp16": false,
+    "use_exllama": true
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 130000,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers.js_config": {
+    "dtype": "q4",
+    "kv_cache_dtype": {
+      "fp16": "float16",
+      "q4f16": "float16"
+    },
+    "use_external_data_format": {
+      "model.onnx": true,
+      "model_fp16.onnx": true
+    }
+  },
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "vocab_size": 49152
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 2,
+  "transformers_version": "4.51.3"
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:b3ca398833ce0d2b0a5f8298117c385131a527374f8fd354c81ec786ac55e77a
+size 1038407104