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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("N-Bot-Int/MistThena7B")
model = AutoModelForCausalLM.from_pretrained("N-Bot-Int/MistThena7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use N-Bot-Int/MistThena7B 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use N-Bot-Int/MistThena7B 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use N-Bot-Int/MistThena7B 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 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 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 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",
    max_seq_length=2048,
)

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

ItsMeDevRoland commited on Mar 30, 2025

Commit

57cfe62

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@@ -30,12 +30,12 @@ metrics:
 # MistThena7B - A.
 - MistThena7B is our brand New AI boasting with An Even Bigger **7B** and Ditching **Llama3.2** for **Mistral** for **lightweight Finetuning**
   And Fast Training and Output. MistThena7B is designed to Ditch its Outer-score and Prioritize Total Roleplaying, Trained with **5x More** Dataset
-  Compared to We used At **OpenElla3-Llama3.2B**, Making this New Model Even More Competitive **Against Hallucinations, and Even More Better
   Textual Generations And Uncensored Output**
 - MistThena7B Model **A** Does not suffer the same Prompting issue with **OpenElla3-Llama3.2B**, however please use ChatML style Prompting For Better
   Experience, And Remember to be aware of bias with the training dataset used, The **AI** model is Under **Apache 2.0** however
-  **WE ARE NOT RESPONSIBLE TO YOUR USAGE, PROMPT AND TYPE IN YOUR OWN ACCORDING/WILL**
 - MistThena7B Model **A** Outperforms OpenElla Family Model, However please keep in mind the Parameter Difference. It Outperforms Testing Benchmarks
   In **Roleplaying and Engaging with RP or Generation of Prompts**, You are Free to release a Benchmark.

 # MistThena7B - A.
 - MistThena7B is our brand New AI boasting with An Even Bigger **7B** and Ditching **Llama3.2** for **Mistral** for **lightweight Finetuning**
   And Fast Training and Output. MistThena7B is designed to Ditch its Outer-score and Prioritize Total Roleplaying, Trained with **5x More** Dataset
+  Compared to What We used At **OpenElla3-Llama3.2B**, Making this New Model Even More Competitive **Against Hallucinations, and Even More Better
   Textual Generations And Uncensored Output**
 - MistThena7B Model **A** Does not suffer the same Prompting issue with **OpenElla3-Llama3.2B**, however please use ChatML style Prompting For Better
   Experience, And Remember to be aware of bias with the training dataset used, The **AI** model is Under **Apache 2.0** however
+  **WE ARE NOT RESPONSIBLE TO YOUR USAGE, PROMPTING, AND WAYS ABOUT HOW YOU USE THE MODEL. PLEASE BE GUIDED OWN ACCORDING/WILL**
 - MistThena7B Model **A** Outperforms OpenElla Family Model, However please keep in mind the Parameter Difference. It Outperforms Testing Benchmarks
   In **Roleplaying and Engaging with RP or Generation of Prompts**, You are Free to release a Benchmark.