Text Generation
Transformers
TensorBoard
Safetensors
mistral
Generated from Trainer
conversational
text-generation-inference
Instructions to use tuannm2914/math-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuannm2914/math-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tuannm2914/math-lora") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tuannm2914/math-lora") model = AutoModelForCausalLM.from_pretrained("tuannm2914/math-lora", 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tuannm2914/math-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tuannm2914/math-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tuannm2914/math-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tuannm2914/math-lora
- SGLang
How to use tuannm2914/math-lora 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 "tuannm2914/math-lora" \ --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": "tuannm2914/math-lora", "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 "tuannm2914/math-lora" \ --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": "tuannm2914/math-lora", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tuannm2914/math-lora with Docker Model Runner:
docker model run hf.co/tuannm2914/math-lora
Commit ·
d4fded6
1
Parent(s): 4e67821
Model save
Browse files- README.md +2 -2
- adapter_config.json +2 -2
- added_tokens.json +3 -0
- all_results.json +8 -8
- eval_results.json +4 -4
- runs/Nov30_17-06-41_hpc-hblab/events.out.tfevents.1701338897.hpc-hblab.1611604.0 +3 -0
- runs/Nov30_17-06-41_hpc-hblab/events.out.tfevents.1701339195.hpc-hblab.1611604.1 +3 -0
- special_tokens_map.json +7 -1
- tokenizer.json +9 -0
- tokenizer_config.json +13 -2
- train_results.json +4 -4
- trainer_state.json +10 -10
- training_args.bin +2 -2
README.md
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 0 | 0 | 1.
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### Framework versions
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This model was trained from scratch on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.4187
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 0 | 0 | 1.4187 |
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### Framework versions
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adapter_config.json
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"rank_pattern": {},
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"task_type": "CAUSAL_LM"
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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added_tokens.json
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all_results.json
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"epoch": 0,
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"eval_loss": 1.4187188148498535,
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"eval_runtime": 58.7586,
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"eval_samples": 1200,
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"eval_samples_per_second": 20.423,
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"eval_steps_per_second": 2.553,
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"train_loss": 0.3546818494796753,
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"train_runtime": 239.2844,
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"train_samples": 1200,
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"train_samples_per_second": 5.015,
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"train_steps_per_second": 0.008
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"eval_samples": 1200,
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runs/Nov30_17-06-41_hpc-hblab/events.out.tfevents.1701338897.hpc-hblab.1611604.0
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special_tokens_map.json
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training_args.bin
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