Text Generation
Transformers
TensorBoard
Safetensors
qwen2
Generated from Trainer
text-generation-inference
Instructions to use archit11/qwen-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use archit11/qwen-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="archit11/qwen-ft")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("archit11/qwen-ft") model = AutoModelForCausalLM.from_pretrained("archit11/qwen-ft") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use archit11/qwen-ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "archit11/qwen-ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "archit11/qwen-ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/archit11/qwen-ft
- SGLang
How to use archit11/qwen-ft 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 "archit11/qwen-ft" \ --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": "archit11/qwen-ft", "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 "archit11/qwen-ft" \ --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": "archit11/qwen-ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use archit11/qwen-ft with Docker Model Runner:
docker model run hf.co/archit11/qwen-ft
qwen-ft
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 5.6514
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: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 800
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 7.7492 | 0.0920 | 50 | 7.5711 |
| 7.4321 | 0.1841 | 100 | 7.3186 |
| 7.0377 | 0.2761 | 150 | 6.9218 |
| 6.8861 | 0.3682 | 200 | 6.7542 |
| 6.7161 | 0.4602 | 250 | 6.5817 |
| 6.4542 | 0.5522 | 300 | 6.3050 |
| 6.3839 | 0.6443 | 350 | 6.2909 |
| 6.1342 | 0.7363 | 400 | 6.0133 |
| 5.9798 | 0.8283 | 450 | 5.8894 |
| 5.9043 | 0.9204 | 500 | 5.8061 |
| 5.889 | 1.0124 | 550 | 5.7376 |
| 5.7778 | 1.1045 | 600 | 5.6900 |
| 5.8077 | 1.1965 | 650 | 5.6643 |
| 5.7363 | 1.2885 | 700 | 5.6539 |
| 5.7524 | 1.3806 | 750 | 5.6515 |
| 5.7289 | 1.4726 | 800 | 5.6514 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1
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docker model run hf.co/archit11/qwen-ft