Instructions to use CreitinGameplays/bloom-3b-conversational with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CreitinGameplays/bloom-3b-conversational with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CreitinGameplays/bloom-3b-conversational") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CreitinGameplays/bloom-3b-conversational") model = AutoModelForCausalLM.from_pretrained("CreitinGameplays/bloom-3b-conversational") 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 CreitinGameplays/bloom-3b-conversational with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CreitinGameplays/bloom-3b-conversational" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CreitinGameplays/bloom-3b-conversational", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CreitinGameplays/bloom-3b-conversational
- SGLang
How to use CreitinGameplays/bloom-3b-conversational 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 "CreitinGameplays/bloom-3b-conversational" \ --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": "CreitinGameplays/bloom-3b-conversational", "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 "CreitinGameplays/bloom-3b-conversational" \ --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": "CreitinGameplays/bloom-3b-conversational", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CreitinGameplays/bloom-3b-conversational with Docker Model Runner:
docker model run hf.co/CreitinGameplays/bloom-3b-conversational
🌸 BLOOM 3b Fine-tuned for Chat Assistant
Run this model on Kaggle Notebook
Model Name: bloom-3b-conversational
Model Architecture: bloom
Short Description: This model is a fine-tuned version of the BLOOM 3b language model, focusing on conversational interactions between an user and an AI assistant.
Intended Use: This model is intended for research purposes and exploration of conversational AI applications. It can be used for tasks like:
- Generating responses to user prompts in a chat assistant setting.
- Creating examples of chatbot interactions for further development.
- Studying the capabilities of language models for conversation.
Limitations:
- Fine-tuning Focus: The model's performance is optimized for the specific format and context of the fine-tuning data. It may not generalize well to significantly different conversation styles or topics.
- Potential Biases: The model may inherit biases from the training data. It's important to be aware of these potential biases and use the model responsibly.
- Limited Factual Accuracy: Language models are still under development and may generate responses that are not entirely factually accurate. It's important to verify information generated by the model with other sources.
- Primarily English: While the model can respond in other languages, the quality and accuracy of its responses may be lower compared to English. This is because the model was primarily fine-tuned on English data.
Specific Input Format:
The model was fine-tuned using a specific input format that goes like this:
<|system|> {system prompt} </s> <|prompter|> {user prompt} </s> <|assistant|> {model response}
Using this format when interacting with the model can improve its performance and generate more relevant responses.
Disclaimer: This model is for research and exploration purposes only. It should not be used in any applications that require high levels of accuracy or reliability.
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