Text Generation
Transformers
TensorBoard
Safetensors
mistral
Generated from Trainer
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use tomjennings100/absumm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tomjennings100/absumm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tomjennings100/absumm")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tomjennings100/absumm") model = AutoModelForCausalLM.from_pretrained("tomjennings100/absumm") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tomjennings100/absumm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tomjennings100/absumm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tomjennings100/absumm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tomjennings100/absumm
- SGLang
How to use tomjennings100/absumm 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 "tomjennings100/absumm" \ --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": "tomjennings100/absumm", "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 "tomjennings100/absumm" \ --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": "tomjennings100/absumm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tomjennings100/absumm with Docker Model Runner:
docker model run hf.co/tomjennings100/absumm
qlora-out
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2423
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.5123 | 0.01 | 1 | 1.5038 |
| 1.3662 | 0.06 | 5 | 1.4103 |
| 1.1836 | 0.11 | 10 | 1.3055 |
| 1.2761 | 0.17 | 15 | 1.2810 |
| 1.1779 | 0.22 | 20 | 1.2696 |
| 1.1242 | 0.28 | 25 | 1.2642 |
| 1.2414 | 0.33 | 30 | 1.2588 |
| 1.1382 | 0.39 | 35 | 1.2555 |
| 1.2094 | 0.45 | 40 | 1.2520 |
| 1.1049 | 0.5 | 45 | 1.2504 |
| 1.1709 | 0.56 | 50 | 1.2487 |
| 1.0981 | 0.61 | 55 | 1.2463 |
| 1.1902 | 0.67 | 60 | 1.2446 |
| 1.1526 | 0.72 | 65 | 1.2446 |
| 1.1319 | 0.78 | 70 | 1.2440 |
| 1.1913 | 0.84 | 75 | 1.2430 |
| 1.1875 | 0.89 | 80 | 1.2424 |
| 1.1454 | 0.95 | 85 | 1.2423 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
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Model tree for tomjennings100/absumm
Base model
mistralai/Mistral-7B-v0.1