Instructions to use ristew/mistral-7b-askhn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ristew/mistral-7b-askhn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ristew/mistral-7b-askhn")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ristew/mistral-7b-askhn") model = AutoModelForCausalLM.from_pretrained("ristew/mistral-7b-askhn") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ristew/mistral-7b-askhn with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ristew/mistral-7b-askhn" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ristew/mistral-7b-askhn", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ristew/mistral-7b-askhn
- SGLang
How to use ristew/mistral-7b-askhn 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 "ristew/mistral-7b-askhn" \ --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": "ristew/mistral-7b-askhn", "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 "ristew/mistral-7b-askhn" \ --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": "ristew/mistral-7b-askhn", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ristew/mistral-7b-askhn with Docker Model Runner:
docker model run hf.co/ristew/mistral-7b-askhn
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: 2.9838
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: 2e-05
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.822 | 0.01 | 1 | 2.7914 |
| 2.4718 | 0.15 | 17 | 2.4825 |
| 2.4643 | 0.31 | 34 | 2.4859 |
| 2.4417 | 0.46 | 51 | 2.4764 |
| 2.4343 | 0.62 | 68 | 2.4696 |
| 2.4312 | 0.77 | 85 | 2.4645 |
| 2.385 | 0.92 | 102 | 2.4511 |
| 1.5771 | 1.05 | 119 | 2.5741 |
| 1.4889 | 1.21 | 136 | 2.5933 |
| 1.4574 | 1.36 | 153 | 2.6168 |
| 1.493 | 1.52 | 170 | 2.6088 |
| 1.4544 | 1.67 | 187 | 2.6049 |
| 1.4422 | 1.82 | 204 | 2.5967 |
| 1.3711 | 1.98 | 221 | 2.6013 |
| 0.7967 | 2.11 | 238 | 3.1609 |
| 0.7342 | 2.26 | 255 | 3.0085 |
| 0.7731 | 2.42 | 272 | 2.9758 |
| 0.7546 | 2.57 | 289 | 2.9832 |
| 0.7936 | 2.72 | 306 | 2.9837 |
| 0.7374 | 2.88 | 323 | 2.9838 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for ristew/mistral-7b-askhn
Base model
mistralai/Mistral-7B-v0.1