Instructions to use tencent/Youtu-LLM-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Youtu-LLM-2B with Transformers:

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

pipe = pipeline("text-generation", model="tencent/Youtu-LLM-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tencent/Youtu-LLM-2B")
model = AutoModelForCausalLM.from_pretrained("tencent/Youtu-LLM-2B")
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 tencent/Youtu-LLM-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Youtu-LLM-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Youtu-LLM-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Youtu-LLM-2B

SGLang

How to use tencent/Youtu-LLM-2B 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 "tencent/Youtu-LLM-2B" \
    --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": "tencent/Youtu-LLM-2B",
		"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 "tencent/Youtu-LLM-2B" \
        --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": "tencent/Youtu-LLM-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tencent/Youtu-LLM-2B with Docker Model Runner:
```
docker model run hf.co/tencent/Youtu-LLM-2B
```

update quickstart

#13

by hevans - opened Jan 7

base: refs/heads/main

←

from: refs/pr/13

Discussion Files changed

+12

-9

Files changed (2) hide show

README.md +11 -9
generation_config.json +1 -0

README.md CHANGED Viewed

@@ -104,7 +104,6 @@ The following example demonstrates how to load the model, enable Reasoning Mode,
 ```python
 import re
-import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 # 1. Configure Model
@@ -123,23 +122,27 @@ prompt = "Hello"
 messages = [{"role": "user", "content": prompt}]
 # Use apply_chat_template to construct input; set enable_thinking=True to activate Reasoning Mode
-input_ids = tokenizer.apply_chat_template(
-    messages,
-    tokenize=True,
-    add_generation_prompt=True,
-    return_tensors="pt",
     enable_thinking=True
-).to(model.device)
 # 4. Generate Response
 outputs = model.generate(
-    input_ids,
     max_new_tokens=512,
     do_sample=True,
     temperature=1.0,
     top_p=0.95,
     repetition_penalty=1.05
 )
 # 5. Parse Results
 full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
@@ -161,7 +164,6 @@ thought, final_answer = parse_reasoning(full_response)
 print(f"\n{'='*20} Thought Process {'='*20}\n{thought}")
 print(f"\n{'='*20} Final Answer {'='*20}\n{final_answer}")
 ```
 ### 3. Key Configuration Details

 ```python
 import re
 from transformers import AutoTokenizer, AutoModelForCausalLM
 # 1. Configure Model
 messages = [{"role": "user", "content": prompt}]
 # Use apply_chat_template to construct input; set enable_thinking=True to activate Reasoning Mode
+input_text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
     enable_thinking=True
+)
+model_inputs = tokenizer([input_text], return_tensors="pt").to(model.device)
+print("Input prepared. Starting generation...")
 # 4. Generate Response
 outputs = model.generate(
+    **model_inputs,
     max_new_tokens=512,
     do_sample=True,
     temperature=1.0,
+    top_k=20,
     top_p=0.95,
     repetition_penalty=1.05
 )
+print("Generation complete!")
 # 5. Parse Results
 full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(f"\n{'='*20} Thought Process {'='*20}\n{thought}")
 print(f"\n{'='*20} Final Answer {'='*20}\n{final_answer}")
 ```
 ### 3. Key Configuration Details

generation_config.json CHANGED Viewed

@@ -2,6 +2,7 @@
     "_from_model_config": true,
     "bos_token_id": 128000,
     "eos_token_id": 128001,
     "do_sample": true,
     "temperature": 1.0,
     "top_k": 20,

     "_from_model_config": true,
     "bos_token_id": 128000,
     "eos_token_id": 128001,
+    "pad_token_id": 128001,
     "do_sample": true,
     "temperature": 1.0,
     "top_k": 20,