Text Generation
Transformers
Safetensors
mistral
custom_code
text-generation-inference
8-bit precision
bitsandbytes
Instructions to use RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits
- SGLang
How to use RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits 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 "RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits" \ --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": "RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits", "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 "RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits" \ --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": "RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits with Docker Model Runner:
docker model run hf.co/RichardErkhov/KnutJaegersberg_-_webMistral-7B-8bits
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
webMistral-7B - bnb 8bits
- Model creator: https://huggingface.co/KnutJaegersberg/
- Original model: https://huggingface.co/KnutJaegersberg/webMistral-7B/
Original model description:
license: cc-by-nc-4.0
Prompt Example:
## Question: What is slowing down our internet speeds?
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### Response: There are several factors that can slow down internet speeds. These include having gadgets with slower internet technology, running background programs[2], using more bandwidth than your plan allows[3], equipment damage[4], an outdated router or a less-than-ideal router location[5], and bandwidth throttling by the internet service provider[5].
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 47.08 |
| ARC (25-shot) | 59.04 |
| HellaSwag (10-shot) | 80.89 |
| MMLU (5-shot) | 59.0 |
| TruthfulQA (0-shot) | 39.71 |
| Winogrande (5-shot) | 76.32 |
| GSM8K (5-shot) | 8.87 |
| DROP (3-shot) | 5.75 |
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