Instructions to use Nabbers1999/Mini-Llama-14B-Chat-SFT-0129 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nabbers1999/Mini-Llama-14B-Chat-SFT-0129 with Transformers:

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

pipe = pipeline("text-generation", model="Nabbers1999/Mini-Llama-14B-Chat-SFT-0129")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nabbers1999/Mini-Llama-14B-Chat-SFT-0129")
model = AutoModelForCausalLM.from_pretrained("Nabbers1999/Mini-Llama-14B-Chat-SFT-0129", 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 Nabbers1999/Mini-Llama-14B-Chat-SFT-0129 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nabbers1999/Mini-Llama-14B-Chat-SFT-0129"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nabbers1999/Mini-Llama-14B-Chat-SFT-0129",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nabbers1999/Mini-Llama-14B-Chat-SFT-0129

SGLang

How to use Nabbers1999/Mini-Llama-14B-Chat-SFT-0129 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 "Nabbers1999/Mini-Llama-14B-Chat-SFT-0129" \
    --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": "Nabbers1999/Mini-Llama-14B-Chat-SFT-0129",
		"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 "Nabbers1999/Mini-Llama-14B-Chat-SFT-0129" \
        --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": "Nabbers1999/Mini-Llama-14B-Chat-SFT-0129",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nabbers1999/Mini-Llama-14B-Chat-SFT-0129 with Docker Model Runner:
```
docker model run hf.co/Nabbers1999/Mini-Llama-14B-Chat-SFT-0129
```

Mini-Llama-14B-Chat-SFT-0129 / README.md

Nabbers1999

Create README.md

9ac5fc3 verified 7 months ago

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	---
	base_model: mistralai/Ministral-3-14B-Base-2512
	library_name: transformers
	tags:
	- ministral-3
	- text-generation
	- instruct
	- llamafied
	- novision
	license: apache-2.0
	language:
	- en
	---

	![Llama_ChatSFT](https://cdn-uploads.huggingface.co/production/uploads/6686e9b5a244c4409d9ddf08/zI68d7K6eelGFV-ID3gia.png)

	# Mini-Llama 14B Chat - 0129

	My instruct model has undergone DoRA SFT on my custom synthetic chat dataset, containing single and multi-round chats containing SFW, NSFW, and Toxic single and multi-round chats. This reinforces the model's uncensored compliance with all prompts and teaches it how to better fill roles assigned to it in the system prompt.

	This model has yet to go through DPO preference training and may still have rough edges.

	** Be aware that this adapter, when used without a system prompt to assign it a role may make up its own role. Meaning if you just say 'Hello' it could resond with 'Hello, how may I assist you?' or it might respond with something like "Hi, my name is Carol and I'm a librarian here to assist you with finding the book you're looking for."

	For the base pretrain, see: [Nabbers1999/Mini-Llama-14B-Base-0124](https://huggingface.co/Nabbers1999/Mini-Llama-14B-Base-0124)

	For the instruct, see: [Nabbers1999/Mini-Llama-14B-Instruct-0124](https://huggingface.co/Nabbers1999/Mini-Llama-14B-Instruct-0124)