Instructions to use TDN-M/tknv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TDN-M/tknv1 with Transformers:

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

pipe = pipeline("text-generation", model="TDN-M/tknv1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TDN-M/tknv1")
model = AutoModelForCausalLM.from_pretrained("TDN-M/tknv1", 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 TDN-M/tknv1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TDN-M/tknv1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TDN-M/tknv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TDN-M/tknv1

SGLang

How to use TDN-M/tknv1 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 "TDN-M/tknv1" \
    --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": "TDN-M/tknv1",
		"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 "TDN-M/tknv1" \
        --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": "TDN-M/tknv1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TDN-M/tknv1 with Docker Model Runner:
```
docker model run hf.co/TDN-M/tknv1
```

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+25

-11

Files changed (1) hide show

README.md +25 -11

README.md CHANGED Viewed

@@ -1,12 +1,26 @@
----
-license: mit
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
----
----
-license: mit
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
 pipeline_tag: text-generation

+---
+license: mit
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+---
+license: mit
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
 pipeline_tag: text-generation