Instructions to use Kaballas/TLetsgo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kaballas/TLetsgo with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kaballas/TLetsgo")
model = AutoModelForCausalLM.from_pretrained("Kaballas/TLetsgo", 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 Kaballas/TLetsgo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Kaballas/TLetsgo

SGLang

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

Docker Model Runner
How to use Kaballas/TLetsgo with Docker Model Runner:
```
docker model run hf.co/Kaballas/TLetsgo
```

Improve language tag

by lbourdois - opened Apr 27, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+58

-45

Files changed (1) hide show

README.md +58 -45

README.md CHANGED Viewed

@@ -1,45 +1,58 @@
----
-base_model:
-- Kaballas/T36Model
-- Qwen/Qwen2.5-7B
-- Locutusque/StockQwen-2.5-7B
-- trollek/Qwen2.5-7B-CySecButler-v0.1
-library_name: transformers
-tags:
-- mergekit
-- merge
----
-# merge
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-## Merge Details
-### Merge Method
-This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) as a base.
-### Models Merged
-The following models were included in the merge:
-* [Kaballas/T36Model](https://huggingface.co/Kaballas/T36Model)
-* [Locutusque/StockQwen-2.5-7B](https://huggingface.co/Locutusque/StockQwen-2.5-7B)
-* [trollek/Qwen2.5-7B-CySecButler-v0.1](https://huggingface.co/trollek/Qwen2.5-7B-CySecButler-v0.1)
-### Configuration
-The following YAML configuration was used to produce this model:
-```yaml
-models:
-  - model: trollek/Qwen2.5-7B-CySecButler-v0.1
-  - model: Locutusque/StockQwen-2.5-7B
-  - model: Kaballas/T36Model
-merge_method: model_stock
-base_model: Qwen/Qwen2.5-7B
-dtype: bfloat16
-```

+---
+base_model:
+- Kaballas/T36Model
+- Qwen/Qwen2.5-7B
+- Locutusque/StockQwen-2.5-7B
+- trollek/Qwen2.5-7B-CySecButler-v0.1
+library_name: transformers
+tags:
+- mergekit
+- merge
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# merge
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+## Merge Details
+### Merge Method
+This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) as a base.
+### Models Merged
+The following models were included in the merge:
+* [Kaballas/T36Model](https://huggingface.co/Kaballas/T36Model)
+* [Locutusque/StockQwen-2.5-7B](https://huggingface.co/Locutusque/StockQwen-2.5-7B)
+* [trollek/Qwen2.5-7B-CySecButler-v0.1](https://huggingface.co/trollek/Qwen2.5-7B-CySecButler-v0.1)
+### Configuration
+The following YAML configuration was used to produce this model:
+```yaml
+models:
+  - model: trollek/Qwen2.5-7B-CySecButler-v0.1
+  - model: Locutusque/StockQwen-2.5-7B
+  - model: Kaballas/T36Model
+merge_method: model_stock
+base_model: Qwen/Qwen2.5-7B
+dtype: bfloat16
+```