Instructions to use Intrinsic-Data/mistral7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Intrinsic-Data/mistral7b-base with Transformers:

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

pipe = pipeline("text-generation", model="Intrinsic-Data/mistral7b-base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Intrinsic-Data/mistral7b-base")
model = AutoModelForCausalLM.from_pretrained("Intrinsic-Data/mistral7b-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Intrinsic-Data/mistral7b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Intrinsic-Data/mistral7b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Intrinsic-Data/mistral7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Intrinsic-Data/mistral7b-base

SGLang

How to use Intrinsic-Data/mistral7b-base 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 "Intrinsic-Data/mistral7b-base" \
    --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": "Intrinsic-Data/mistral7b-base",
		"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 "Intrinsic-Data/mistral7b-base" \
        --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": "Intrinsic-Data/mistral7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Intrinsic-Data/mistral7b-base with Docker Model Runner:
```
docker model run hf.co/Intrinsic-Data/mistral7b-base
```

mistral7b-base / README.md

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---
license: apache-2.0
pipeline_tag: text-generation
language:
  - en
tags:
- pretrained
inference:
  parameters:
    temperature: 0.7
---
# Model Card for Mistral-7B-v0.1

The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. 
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.

For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).

## Model Architecture

Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer

## Troubleshooting

- If you see the following error:
```
KeyError: 'mistral'
```
- Or:
```
NotImplementedError: Cannot copy out of meta tensor; no data!
```

Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.

## Notice

Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.

## The Mistral AI Team
 
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

	---
	license: apache-2.0
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- pretrained
	inference:
	parameters:
	temperature: 0.7
	---

	# Model Card for Mistral-7B-v0.1

	The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
	Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.

	For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).

	## Model Architecture

	Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
	- Grouped-Query Attention
	- Sliding-Window Attention
	- Byte-fallback BPE tokenizer

	## Troubleshooting

	- If you see the following error:
	```
	KeyError: 'mistral'
	```
	- Or:
	```
	NotImplementedError: Cannot copy out of meta tensor; no data!
	```

	Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.

	## Notice

	Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.

	## The Mistral AI Team

	Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.