Instructions to use N-Bot-Int/MistThena7B-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/MistThena7B-V2 with Transformers:

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

pipe = pipeline("text-generation", model="N-Bot-Int/MistThena7B-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("N-Bot-Int/MistThena7B-V2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use N-Bot-Int/MistThena7B-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N-Bot-Int/MistThena7B-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N-Bot-Int/MistThena7B-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/N-Bot-Int/MistThena7B-V2

SGLang

How to use N-Bot-Int/MistThena7B-V2 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 "N-Bot-Int/MistThena7B-V2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N-Bot-Int/MistThena7B-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "N-Bot-Int/MistThena7B-V2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N-Bot-Int/MistThena7B-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use N-Bot-Int/MistThena7B-V2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for N-Bot-Int/MistThena7B-V2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for N-Bot-Int/MistThena7B-V2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for N-Bot-Int/MistThena7B-V2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="N-Bot-Int/MistThena7B-V2",
    max_seq_length=2048,
)

Docker Model Runner
How to use N-Bot-Int/MistThena7B-V2 with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/MistThena7B-V2
```

ItsMeDevRoland commited on Apr 14, 2025

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@@ -40,6 +40,10 @@ metrics:
 - Activate MistThena's Expanded Actions, by mirroring it(ie using Emoji on your own prompts), to ensure MistThena's
   Use of Emoji or Actions!
 - MistThena7B contains more Fine-tuned Dataset so please Report any issues found through our email
   [nexus.networkinteractives@gmail.com](mailto:nexus.networkinteractives@gmail.com)
   about any overfitting, or improvements for the future Model **V3**,

 - Activate MistThena's Expanded Actions, by mirroring it(ie using Emoji on your own prompts), to ensure MistThena's
   Use of Emoji or Actions!
+- MistThena7B-V2 is also trained on 160K Examples from our Latest Corpus **IRIS_UNCENSORED_R2**!, This shows that
+  Iris_Uncensored_R2 shows huge potential to produce good outputs, and reveals MistThena7B's Good Roleplaying capabilities,
+  Which were obtained through High and rigorous training, combined with Preventive measure to overfitting
 - MistThena7B contains more Fine-tuned Dataset so please Report any issues found through our email
   [nexus.networkinteractives@gmail.com](mailto:nexus.networkinteractives@gmail.com)
   about any overfitting, or improvements for the future Model **V3**,