Instructions to use BeaverAI/mistral-doryV2-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BeaverAI/mistral-doryV2-12b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BeaverAI/mistral-doryV2-12b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BeaverAI/mistral-doryV2-12b") model = AutoModelForCausalLM.from_pretrained("BeaverAI/mistral-doryV2-12b") 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 BeaverAI/mistral-doryV2-12b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BeaverAI/mistral-doryV2-12b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BeaverAI/mistral-doryV2-12b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/BeaverAI/mistral-doryV2-12b
- SGLang
How to use BeaverAI/mistral-doryV2-12b 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 "BeaverAI/mistral-doryV2-12b" \ --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": "BeaverAI/mistral-doryV2-12b", "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 "BeaverAI/mistral-doryV2-12b" \ --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": "BeaverAI/mistral-doryV2-12b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use BeaverAI/mistral-doryV2-12b with Docker Model Runner:
docker model run hf.co/BeaverAI/mistral-doryV2-12b
Dory 12b (v2)
(redone) redone instruct finetune of mistral nemo 12b's base. not (E)RP-focused, leave that to drummer.
thanks to twisted again for the compute :3
Prompting
alpaca-like:
### System:
[Optional system prompt]
### Instruction:
[Query]
### Response:
[Response]</s>
### Instruction:
[...]
Training details
Rank 64 QDoRA, trained on the following data mix:
- All of kalomaze/Opus_Instruct_3k
- All conversations with a reward model rating above 5 in Magpie-Align/Magpie-Gemma2-Pro-Preview-Filtered
- 50k of Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- All stories above 4.7 rating and published before 2020 in Fizzarolli/FallingThroughTheSkies-592k-Filtered-Filtered
- Downloads last month
- 30
Model tree for BeaverAI/mistral-doryV2-12b
Base model
mistralai/Mistral-Nemo-Base-2407