Instructions to use AI-AgentSafa/ministral-tsql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AI-AgentSafa/ministral-tsql with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-3-8B-Instruct-2512-BF16")
model = PeftModel.from_pretrained(base_model, "AI-AgentSafa/ministral-tsql")

Transformers

How to use AI-AgentSafa/ministral-tsql with Transformers:

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

pipe = pipeline("text-generation", model="AI-AgentSafa/ministral-tsql")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AI-AgentSafa/ministral-tsql")
model = AutoModelForMultimodalLM.from_pretrained("AI-AgentSafa/ministral-tsql")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AI-AgentSafa/ministral-tsql with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AI-AgentSafa/ministral-tsql

SGLang

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

Docker Model Runner
How to use AI-AgentSafa/ministral-tsql with Docker Model Runner:
```
docker model run hf.co/AI-AgentSafa/ministral-tsql
```

See axolotl config

axolotl version: 0.16.0.dev0

adapter: lora
base_model: mistralai/Ministral-3-8B-Instruct-2512-BF16
bf16: true
datasets:
- path: AI-AgentSafa/dataset
  type: alpaca
gradient_accumulation_steps: 2
learning_rate: 0.0002
load_in_4bit: false
lora_alpha: 32
lora_dropout: 0.05
lora_r: 16
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
micro_batch_size: 4
num_epochs: 5
optimizer: adamw_bnb_8bit
output_dir: /workspace/outputs/ministral8b-tsql
sequence_len: 2048
train_on_inputs: false

workspace/outputs/ministral8b-tsql

This model is a fine-tuned version of mistralai/Ministral-3-8B-Instruct-2512-BF16 on the AI-AgentSafa/dataset dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 4
training_steps: 144

Training results

Framework versions

PEFT 0.18.1
Transformers 5.5.0
Pytorch 2.8.0+cu128
Datasets 4.5.0
Tokenizers 0.22.2

Downloads last month: 2

Model tree for AI-AgentSafa/ministral-tsql

Base model

mistralai/Ministral-3-8B-Base-2512

Finetuned

mistralai/Ministral-3-8B-Instruct-2512-BF16

Adapter

(5)

this model