Instructions to use EhDa24/MNLP_M2_mcqa_model_full_ft2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EhDa24/MNLP_M2_mcqa_model_full_ft2 with Transformers:

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

pipe = pipeline("text-generation", model="EhDa24/MNLP_M2_mcqa_model_full_ft2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EhDa24/MNLP_M2_mcqa_model_full_ft2")
model = AutoModelForCausalLM.from_pretrained("EhDa24/MNLP_M2_mcqa_model_full_ft2")
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 EhDa24/MNLP_M2_mcqa_model_full_ft2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EhDa24/MNLP_M2_mcqa_model_full_ft2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EhDa24/MNLP_M2_mcqa_model_full_ft2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EhDa24/MNLP_M2_mcqa_model_full_ft2

SGLang

How to use EhDa24/MNLP_M2_mcqa_model_full_ft2 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 "EhDa24/MNLP_M2_mcqa_model_full_ft2" \
    --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": "EhDa24/MNLP_M2_mcqa_model_full_ft2",
		"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 "EhDa24/MNLP_M2_mcqa_model_full_ft2" \
        --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": "EhDa24/MNLP_M2_mcqa_model_full_ft2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EhDa24/MNLP_M2_mcqa_model_full_ft2 with Docker Model Runner:
```
docker model run hf.co/EhDa24/MNLP_M2_mcqa_model_full_ft2
```

EhDa24 commited on May 20, 2025

Commit

cedaafc

verified ·

1 Parent(s): 4581e1e

End of training

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -5,14 +5,14 @@ base_model: Qwen/Qwen3-0.6B-Base
 tags:
 - generated_from_trainer
 model-index:
-- name: MNLP_M2_mcqa_model_full_ft3
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
-# MNLP_M2_mcqa_model_full_ft3
 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
@@ -41,7 +41,7 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 32
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- num_epochs: 18
 - mixed_precision_training: Native AMP
 ### Training results

 tags:
 - generated_from_trainer
 model-index:
+- name: MNLP_M2_mcqa_model_full_ft2
   results: []
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
+# MNLP_M2_mcqa_model_full_ft2
 This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
 - total_train_batch_size: 32
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
+- num_epochs: 4
 - mixed_precision_training: Native AMP
 ### Training results