Text Generation
Transformers
Safetensors
Romanian
English
rost
romanian
bilingual
nanochat
conversational
custom_code
Instructions to use stefaniancu/rost-1b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stefaniancu/rost-1b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="stefaniancu/rost-1b-instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("stefaniancu/rost-1b-instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use stefaniancu/rost-1b-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stefaniancu/rost-1b-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "stefaniancu/rost-1b-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/stefaniancu/rost-1b-instruct
- SGLang
How to use stefaniancu/rost-1b-instruct 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 "stefaniancu/rost-1b-instruct" \ --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": "stefaniancu/rost-1b-instruct", "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 "stefaniancu/rost-1b-instruct" \ --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": "stefaniancu/rost-1b-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use stefaniancu/rost-1b-instruct with Docker Model Runner:
docker model run hf.co/stefaniancu/rost-1b-instruct
Commit History
8 days ago
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Model card: repository moved from rostlabs to stefaniancu; update IDs and links
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Update README.md
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The weights are bfloat16 now; say so instead of explaining float32
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Remove the float32 shards, superseded by model.safetensors
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Remove the float32 shards, superseded by model.safetensors
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Remove the float32 shards, superseded by model.safetensors
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Declare bfloat16
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Publish the weights in bfloat16, the precision they were trained in
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The fork moved to the rostlabs org; drop links into a private repo
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GGUF and llama.cpp are available now; say which runtimes are not
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Say which decoding settings to use, and why 1.1
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Ship the decoding settings, and a stop condition
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Rewrite the card: evaluation, intended use, limitations
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Update README.md
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initial commit
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