Instructions to use leafspark/Mistral-Large-218B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leafspark/Mistral-Large-218B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="leafspark/Mistral-Large-218B-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("leafspark/Mistral-Large-218B-Instruct")
model = AutoModelForCausalLM.from_pretrained("leafspark/Mistral-Large-218B-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use leafspark/Mistral-Large-218B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leafspark/Mistral-Large-218B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leafspark/Mistral-Large-218B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/leafspark/Mistral-Large-218B-Instruct

SGLang

How to use leafspark/Mistral-Large-218B-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 "leafspark/Mistral-Large-218B-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": "leafspark/Mistral-Large-218B-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 "leafspark/Mistral-Large-218B-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": "leafspark/Mistral-Large-218B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use leafspark/Mistral-Large-218B-Instruct with Docker Model Runner:
```
docker model run hf.co/leafspark/Mistral-Large-218B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mistral-Large-218B-Instruct

Mistral-Large-218B-Instruct is a dense Large Language Model (LLM) with 218 billion parameters. Self-merged from the original Mistral Large 2.

Key features

218 billion parameters
Multi-lingual support for dozens of languages
Trained on 80+ coding languages
128k context window
Mistral Research License: Allows usage and modification for research and non-commercial purposes

Hardware Requirements

Given the size of this model (218B parameters), it requires substantial computational resources for inference:

Recommended: 8xH100 (640GB)
Alternatively: Distributed inference setup across multiple machines

Limitations

No built-in moderation mechanisms
Computationally expensive inference
May exhibit biases present in training data
Outputs should be critically evaluated for sensitive applications

Notes

This was just a fun testing model, merged with the merge.py script in the base of the repo.

Quants

GGUF: mradermacher/Mistral-Large-218B-Instruct-GGUF

imatrix GGUF: mradermacher/Mistral-Large-218B-Instruct-i1-GGUF

Compatible mergekit config:

slices:
- sources:
  - layer_range: [0, 20]
    model: mistralai/Mistral-Large-Instruct-2407
- sources:
  - layer_range: [10, 30]
    model: mistralai/Mistral-Large-Instruct-2407
- sources:
  - layer_range: [20, 40]
    model: mistralai/Mistral-Large-Instruct-2407
- sources:
  - layer_range: [30, 50]
    model: mistralai/Mistral-Large-Instruct-2407
- sources:
  - layer_range: [40, 60]
    model: mistralai/Mistral-Large-Instruct-2407
- sources:
  - layer_range: [50, 70]
    model: mistralai/Mistral-Large-Instruct-2407
- sources:
  - layer_range: [60, 80]
    model: mistralai/Mistral-Large-Instruct-2407
- sources:
  - layer_range: [70, 87]
    model: mistralai/Mistral-Large-Instruct-2407
merge_method: passthrough
dtype: bfloat16

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Safetensors

Model size

218B params

Tensor type

BF16

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