Instructions to use pankajmathur/Mistral-7B-model_45k6e2e4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajmathur/Mistral-7B-model_45k6e2e4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pankajmathur/Mistral-7B-model_45k6e2e4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pankajmathur/Mistral-7B-model_45k6e2e4")
model = AutoModelForCausalLM.from_pretrained("pankajmathur/Mistral-7B-model_45k6e2e4")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pankajmathur/Mistral-7B-model_45k6e2e4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajmathur/Mistral-7B-model_45k6e2e4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajmathur/Mistral-7B-model_45k6e2e4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pankajmathur/Mistral-7B-model_45k6e2e4

SGLang

How to use pankajmathur/Mistral-7B-model_45k6e2e4 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 "pankajmathur/Mistral-7B-model_45k6e2e4" \
    --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": "pankajmathur/Mistral-7B-model_45k6e2e4",
		"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 "pankajmathur/Mistral-7B-model_45k6e2e4" \
        --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": "pankajmathur/Mistral-7B-model_45k6e2e4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pankajmathur/Mistral-7B-model_45k6e2e4 with Docker Model Runner:
```
docker model run hf.co/pankajmathur/Mistral-7B-model_45k6e2e4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral-7B-model_45k6e2e4

This model is trained on Mistral-7B-v0.1

License Disclaimer:

This model is released under Apache 2.0, and comes with no warranty or guarantees of any kind.

Limitations & Biases:

While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.

Despite diligent efforts in refining the training data, there remains a possibility for the generation of inappropriate, biased, or offensive content.

Exercise caution and cross-check information when necessary. This is an uncensored model.

Citation:

Please kindly cite using the following BibTeX:

@misc{Mistral-7B-model_45k6e2e4,
author = {Pankaj Mathur},
title = {Mistral-7B-model_45k6e2e4: An Orca style Mistral-7B-v0.1 model},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/pankajmathur/Mistral-7B-model_45k6e2e4},
}

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Safetensors

Model size

7B params

Tensor type

BF16

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Quantizations

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Mistral-7B-model_45k6e2e4

Mistral-7B-model_45k6e2e4

License Disclaimer:

Limitations & Biases:

Citation:

Model tree for pankajmathur/Mistral-7B-model_45k6e2e4

Datasets used to train pankajmathur/Mistral-7B-model_45k6e2e4