Instructions to use thejagstudio/LLMTuner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thejagstudio/LLMTuner-v1 with Transformers:

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

pipe = pipeline("text-generation", model="thejagstudio/LLMTuner-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thejagstudio/LLMTuner-v1", device_map="auto")

PEFT
How to use thejagstudio/LLMTuner-v1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thejagstudio/LLMTuner-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/thejagstudio/LLMTuner-v1

SGLang

How to use thejagstudio/LLMTuner-v1 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 "thejagstudio/LLMTuner-v1" \
    --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": "thejagstudio/LLMTuner-v1",
		"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 "thejagstudio/LLMTuner-v1" \
        --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": "thejagstudio/LLMTuner-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thejagstudio/LLMTuner-v1 with Docker Model Runner:
```
docker model run hf.co/thejagstudio/LLMTuner-v1
```

LLMTuner-v1 / tokenizer_config.json

thejagstudio

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	{
	"add_prefix_space": false,
	"added_tokens_decoder": {
	"128245": {
	"content": "<s>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"128247": {
	"content": "</s>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151643": {
	"content": "<\|endoftext\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151644": {
	"content": "<\|im_start\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151645": {
	"content": "<\|im_end\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151646": {
	"content": "<pad>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151647": {
	"content": "<\|user\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151648": {
	"content": "<\|assistant\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"151649": {
	"content": "<\|system\|>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"additional_special_tokens": [
	"<\|user\|>",
	"<\|assistant\|>",
	"<\|system\|>",
	"</s>",
	"<s>",
	"<pad>"
	],
	"bos_token": "<s>",
	"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<\|user\|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<\|system\|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<\|assistant\|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<\|assistant\|>' }}\n{% endif %}\n{% endfor %}",
	"clean_up_tokenization_spaces": false,
	"eos_token": "</s>",
	"errors": "replace",
	"model_max_length": 2048,
	"pad_token": "<pad>",
	"split_special_tokens": false,
	"tokenizer_class": "Qwen2Tokenizer",
	"unk_token": null
	}