Instructions to use rickson21/qwen2.5-32b-finmin-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rickson21/qwen2.5-32b-finmin-v1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-32B-Instruct")
model = PeftModel.from_pretrained(base_model, "rickson21/qwen2.5-32b-finmin-v1")

Transformers

How to use rickson21/qwen2.5-32b-finmin-v1 with Transformers:

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

pipe = pipeline("text-generation", model="rickson21/qwen2.5-32b-finmin-v1")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rickson21/qwen2.5-32b-finmin-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rickson21/qwen2.5-32b-finmin-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rickson21/qwen2.5-32b-finmin-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rickson21/qwen2.5-32b-finmin-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rickson21/qwen2.5-32b-finmin-v1

SGLang

How to use rickson21/qwen2.5-32b-finmin-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 "rickson21/qwen2.5-32b-finmin-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rickson21/qwen2.5-32b-finmin-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rickson21/qwen2.5-32b-finmin-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rickson21/qwen2.5-32b-finmin-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rickson21/qwen2.5-32b-finmin-v1 with Docker Model Runner:
```
docker model run hf.co/rickson21/qwen2.5-32b-finmin-v1
```

qwen2.5-32b-finmin-v1 / checkpoint-15 /trainer_state.json

rickson21

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	{
	"best_global_step": 15,
	"best_metric": 0.4123309552669525,
	"best_model_checkpoint": "finetuned_models/runpod-qwen-32b-3epochs_202512121522/checkpoint-15",
	"epoch": 1.0714285714285714,
	"eval_steps": 5,
	"global_step": 15,
	"is_hyper_param_search": false,
	"is_local_process_zero": true,
	"is_world_process_zero": true,
	"log_history": [
	{
	"epoch": 0.35714285714285715,
	"eval_loss": 9.715937614440918,
	"eval_runtime": 80.6928,
	"eval_samples_per_second": 0.459,
	"eval_steps_per_second": 0.459,
	"step": 5
	},
	{
	"epoch": 0.7142857142857143,
	"eval_loss": 0.6401041150093079,
	"eval_runtime": 80.8795,
	"eval_samples_per_second": 0.457,
	"eval_steps_per_second": 0.457,
	"step": 10
	},
	{
	"epoch": 1.0714285714285714,
	"eval_loss": 0.4123309552669525,
	"eval_runtime": 80.9572,
	"eval_samples_per_second": 0.457,
	"eval_steps_per_second": 0.457,
	"step": 15
	}
	],
	"logging_steps": 20,
	"max_steps": 42,
	"num_input_tokens_seen": 0,
	"num_train_epochs": 3,
	"save_steps": 5,
	"stateful_callbacks": {
	"TrainerControl": {
	"args": {
	"should_epoch_stop": false,
	"should_evaluate": false,
	"should_log": false,
	"should_save": true,
	"should_training_stop": false
	},
	"attributes": {}
	}
	},
	"total_flos": 7.190331646817894e+16,
	"train_batch_size": 1,
	"trial_name": null,
	"trial_params": null
	}