Instructions to use selili688/tiny-chatbot-model-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use selili688/tiny-chatbot-model-dpo with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "selili688/tiny-chatbot-model-dpo") - Transformers
How to use selili688/tiny-chatbot-model-dpo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="selili688/tiny-chatbot-model-dpo") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("selili688/tiny-chatbot-model-dpo", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use selili688/tiny-chatbot-model-dpo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "selili688/tiny-chatbot-model-dpo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "selili688/tiny-chatbot-model-dpo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/selili688/tiny-chatbot-model-dpo
- SGLang
How to use selili688/tiny-chatbot-model-dpo 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 "selili688/tiny-chatbot-model-dpo" \ --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": "selili688/tiny-chatbot-model-dpo", "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 "selili688/tiny-chatbot-model-dpo" \ --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": "selili688/tiny-chatbot-model-dpo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use selili688/tiny-chatbot-model-dpo with Docker Model Runner:
docker model run hf.co/selili688/tiny-chatbot-model-dpo
DPO adapter
Browse files- README.md +6 -2
- special_tokens_map.json +1 -7
README.md
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---
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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library_name:
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model_name: tiny-chatbot-model-dpo
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tags:
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- dpo
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- trl
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licence: license
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---
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# Model Card for tiny-chatbot-model-dpo
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### Framework versions
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- TRL: 0.21.0
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- Transformers: 4.55.0
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- Pytorch: 2.6.0+cu124
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---
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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library_name: peft
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model_name: tiny-chatbot-model-dpo
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tags:
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- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
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- dpo
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- lora
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- transformers
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- trl
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licence: license
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pipeline_tag: text-generation
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---
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# Model Card for tiny-chatbot-model-dpo
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### Framework versions
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- PEFT 0.17.0
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- TRL: 0.21.0
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- Transformers: 4.55.0
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- Pytorch: 2.6.0+cu124
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special_tokens_map.json
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"rstrip": false,
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"single_word": false
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},
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"pad_token":
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "</s>",
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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