Instructions to use tiny-random/qwen2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/qwen2.5 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/qwen2.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/qwen2.5")
model = AutoModelForCausalLM.from_pretrained("tiny-random/qwen2.5", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/qwen2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/qwen2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/qwen2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/qwen2.5

SGLang

How to use tiny-random/qwen2.5 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 "tiny-random/qwen2.5" \
    --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": "tiny-random/qwen2.5",
		"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 "tiny-random/qwen2.5" \
        --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": "tiny-random/qwen2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/qwen2.5 with Docker Model Runner:
```
docker model run hf.co/tiny-random/qwen2.5
```

yujiepan commited on Apr 15

Commit

7a6a312

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README.md CHANGED Viewed

@@ -112,6 +112,40 @@ model.save_pretrained(save_folder)
 </details>
 ### Test environment:

 </details>
+### Printing the model:
+<details><summary>Click to expand</summary>
+```text
+Qwen2ForCausalLM(
+  (model): Qwen2Model(
+    (embed_tokens): Embedding(152064, 8)
+    (layers): ModuleList(
+      (0-3): 4 x Qwen2DecoderLayer(
+        (self_attn): Qwen2Attention(
+          (q_proj): Linear(in_features=8, out_features=256, bias=True)
+          (k_proj): Linear(in_features=8, out_features=128, bias=True)
+          (v_proj): Linear(in_features=8, out_features=128, bias=True)
+          (o_proj): Linear(in_features=256, out_features=8, bias=False)
+        )
+        (mlp): Qwen2MLP(
+          (gate_proj): Linear(in_features=8, out_features=32, bias=False)
+          (up_proj): Linear(in_features=8, out_features=32, bias=False)
+          (down_proj): Linear(in_features=32, out_features=8, bias=False)
+          (act_fn): SiLUActivation()
+        )
+        (input_layernorm): Qwen2RMSNorm((8,), eps=1e-06)
+        (post_attention_layernorm): Qwen2RMSNorm((8,), eps=1e-06)
+      )
+    )
+    (norm): Qwen2RMSNorm((8,), eps=1e-06)
+    (rotary_emb): Qwen2RotaryEmbedding()
+  )
+  (lm_head): Linear(in_features=8, out_features=152064, bias=False)
+)
+```
+</details>
 ### Test environment: