Text Generation
Transformers
Safetensors
qwen3
unsloth
Generated from Trainer
text-generation-inference
Instructions to use andresnowak/MNLP_M3_mcqa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andresnowak/MNLP_M3_mcqa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="andresnowak/MNLP_M3_mcqa_model", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("andresnowak/MNLP_M3_mcqa_model") model = AutoModelForCausalLM.from_pretrained("andresnowak/MNLP_M3_mcqa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use andresnowak/MNLP_M3_mcqa_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "andresnowak/MNLP_M3_mcqa_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "andresnowak/MNLP_M3_mcqa_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/andresnowak/MNLP_M3_mcqa_model
- SGLang
How to use andresnowak/MNLP_M3_mcqa_model 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 "andresnowak/MNLP_M3_mcqa_model" \ --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": "andresnowak/MNLP_M3_mcqa_model", "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 "andresnowak/MNLP_M3_mcqa_model" \ --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": "andresnowak/MNLP_M3_mcqa_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use andresnowak/MNLP_M3_mcqa_model with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for andresnowak/MNLP_M3_mcqa_model to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for andresnowak/MNLP_M3_mcqa_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for andresnowak/MNLP_M3_mcqa_model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="andresnowak/MNLP_M3_mcqa_model", max_seq_length=2048, ) - Docker Model Runner
How to use andresnowak/MNLP_M3_mcqa_model with Docker Model Runner:
docker model run hf.co/andresnowak/MNLP_M3_mcqa_model
End of training
Browse files- README.md +57 -0
- generation_config.json +8 -0
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: unsloth/Qwen3-0.6B-Base
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tags:
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- unsloth
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- generated_from_trainer
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model-index:
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- name: Qwen3-0.6B-mcqa_model_2
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Qwen3-0.6B-mcqa_model_2
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This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base) on an unknown dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 32
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- total_train_batch_size: 64
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- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.04
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- num_epochs: 2
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### Training results
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### Framework versions
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- Transformers 4.52.4
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- Pytorch 2.7.0+cu126
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- Datasets 3.6.0
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- Tokenizers 0.21.0
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generation_config.json
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{
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"max_length": 32768,
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"max_new_tokens": 2048,
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"pad_token_id": 151654,
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"transformers_version": "4.52.4"
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}
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