Instructions to use robbiemu/salamandra-2b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robbiemu/salamandra-2b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="robbiemu/salamandra-2b-instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("robbiemu/salamandra-2b-instruct") model = AutoModelForCausalLM.from_pretrained("robbiemu/salamandra-2b-instruct", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use robbiemu/salamandra-2b-instruct with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf robbiemu/salamandra-2b-instruct:Q4_K_M # Run inference directly in the terminal: llama cli -hf robbiemu/salamandra-2b-instruct:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf robbiemu/salamandra-2b-instruct:Q4_K_M # Run inference directly in the terminal: llama cli -hf robbiemu/salamandra-2b-instruct:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf robbiemu/salamandra-2b-instruct:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf robbiemu/salamandra-2b-instruct:Q4_K_M
Use Docker
docker model run hf.co/robbiemu/salamandra-2b-instruct:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use robbiemu/salamandra-2b-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "robbiemu/salamandra-2b-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": "robbiemu/salamandra-2b-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/robbiemu/salamandra-2b-instruct:Q4_K_M
- SGLang
How to use robbiemu/salamandra-2b-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 "robbiemu/salamandra-2b-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": "robbiemu/salamandra-2b-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 "robbiemu/salamandra-2b-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": "robbiemu/salamandra-2b-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use robbiemu/salamandra-2b-instruct with Ollama:
ollama run hf.co/robbiemu/salamandra-2b-instruct:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use robbiemu/salamandra-2b-instruct with Docker Model Runner:
docker model run hf.co/robbiemu/salamandra-2b-instruct:Q4_K_M
- Lemonade
How to use robbiemu/salamandra-2b-instruct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull robbiemu/salamandra-2b-instruct:Q4_K_M
Run and chat with the model
lemonade run user.salamandra-2b-instruct-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Commit ·
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## Ethical Considerations and Limitations
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---
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## Ethical Considerations and Limitations
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We examine the presence of undesired societal and cognitive biases present in this model using different benchmarks. For societal biases, we test performance using the BBQ dataset (Parrish et al., 2022) in the original English and the Regard dataset (Sheng et al., 2019). We report that moderate accuracies (between 0.5 and 0.6 depending on the social groups) in disambiguated settings, the model performs very poorly in ambiguous setting. Taken together, these results suggest the pervasiveness of social biases that may have an effect on task performance
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Our cognitive bias analysis focuses on positional effects in 0-shot settings, and majority class bias in few-shot settings. For positional effects, we leverage the ARC Multiple Choice Question dataset (Clark et al., 2018). We observe significant, but moderate weak primacy effects, whereby the model shows a preference for answers towards the beginning of the list of provided answers. We measure effects of majority class effects in few-shot settings using SST-2 (Socher et al., 2013). We again detect significant effects, with a small effect size. This suggests that the model is relatively robust against the examined cognitive biases.
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We highlight that our analyses of these biases are by no means exhaustive and are limited by the relative scarcity of adequate resources in all languages present in the training data. We aim to gradually extend and expand our analyses in future work.
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These results can be expected from a model that has undergone only a preliminary instruction tuning. These tests are performed in order to show the biases the model may contain. We urge developers to take them into account and perform safety testing and tuning tailored to their specific applications of the model.
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