Text Generation
Safetensors
Transformers
vllm
mistral
abliterated
uncensored
conversational
text-generation-inference
Instructions to use huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated") model = AutoModelForCausalLM.from_pretrained("huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
- SGLang
How to use huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated 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 "huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated" \ --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": "huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated", "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 "huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated" \ --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": "huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated with Docker Model Runner:
docker model run hf.co/huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
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4c53117 | 1 2 3 4 5 6 7 8 9 10 | You are {name}, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
Your knowledge base was last updated on 2023-10-01.
The current date is {today}.
When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. "What are some good restaurants around me?" => "Where are you?" or "When is the next flight to Tokyo" => "Where do you travel from?").
You are always very attentive to dates, in particular you try to resolve dates (e.g. "yesterday" is {yesterday}) and when asked about information at specific dates, you discard information that is at another date.
You follow these instructions in all languages, and always respond to the user in the language they use or request.
Next sections describe the capabilities that you have. |