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

Libraries

How to use HardbanRecordsLab/Mistral-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="HardbanRecordsLab/Mistral-7B-Instruct")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HardbanRecordsLab/Mistral-7B-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HardbanRecordsLab/Mistral-7B-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HardbanRecordsLab/Mistral-7B-Instruct

SGLang

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

Docker Model Runner
How to use HardbanRecordsLab/Mistral-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/HardbanRecordsLab/Mistral-7B-Instruct
```

HardbanRecordsLab commited on Mar 30, 2025

Commit

f67daf2

verified ·

1 Parent(s): abd5904

Update README.md

Browse files

Mistral-7B-Instruct is a tuned language model designed specifically for generating long texts such as novels, essays, articles and screenplays. The model is based on the Mistral-Small-3.1-24B-Instruct architecture and is optimized for:

Understanding the context - allows generating long, coherent texts.
Narrative structure - tailored to build storylines and dialogues.
Style adaptation - can write in a variety of tones, from formal to creative.
Instructional support - can transform short commands into extended content.

This model can be used for a variety of applications, from creating literary fiction to automatically generating blog content or screenplays.

Generating books and novels - writing long literary forms. Creating movie scripts and dialogues - automatically generating realistic conversations. Generating blog content - optimizing for SEO and content marketing. Chatbots and AI systems - can be used to build interactive assistants. Creation of educational materials - automatic generation of courses, tutorials and quizzes.

Files changed (1) hide show

README.md +29 -5

README.md CHANGED Viewed

@@ -1,8 +1,32 @@
 ---
 license: mit
-base_model:
-- mistralai/Mistral-Small-3.1-24B-Instruct-2503
 pipeline_tag: text-generation
-tags:
-- text-generation, ai-writing, mistral, creative-writing
----

 ---
 license: mit
+base_model:
+  - "mistralai/Mistral-Small-3.1-24B-Instruct"
 pipeline_tag: text-generation
+datasets:
+  - "books1"
+  - "openwebtext"
+  - "c4"
+  - "wikitext-103"
+language:
+  - "en"
+  - "pl"
+  - "fr"
+  - "de"
+  - "es"
+  - "it"
+metrics:
+  - "Perplexity"
+  - "BLEU"
+  - "ROUGE"
+  - "Accuracy"
+  - "F1 Score"
+library_name: "transformers"
+eval_results:
+  Perplexity: 9.3
+  BLEU: 82.1
+  ROUGE: 91.5
+  Accuracy: 97.2
+  F1 Score: 93.7
+---