Instructions to use FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B with Transformers:

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

pipe = pipeline("text-generation", model="FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B")
model = AutoModelForCausalLM.from_pretrained("FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B", 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 FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B

SGLang

How to use FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B 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 "FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B" \
    --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": "FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B",
		"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 "FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B" \
        --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": "FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B with Docker Model Runner:
```
docker model run hf.co/FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B
```

fleet-sft-full

This model is a fine-tuned version of Qwen/Qwen3-32B on the fleet_trajectories_train dataset. It achieves the following results on the evaluation set:

Loss: 0.6065

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: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	1.0228
0.8247	0.0826	10	0.7420
0.7588	0.1652	20	0.6738
0.6626	0.2478	30	0.6482
0.6548	0.3304	40	0.6332
0.6488	0.4130	50	0.6242
0.6595	0.4956	60	0.6179
0.6359	0.5782	70	0.6137
0.6445	0.6608	80	0.6109
0.6231	0.7434	90	0.6087
0.6402	0.8260	100	0.6073
0.6321	0.9086	110	0.6066
0.6315	0.9912	120	0.6065

Framework versions

Transformers 4.52.4
Pytorch 2.10.0+cu128
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 7

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for FleetAI/fleet-sft-wallst-fostgres-Qwen3-32B

Base model

Qwen/Qwen3-32B

Finetuned

(523)

this model