Instructions to use Deathsquad10/Tinyllama2.0-test3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deathsquad10/Tinyllama2.0-test3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deathsquad10/Tinyllama2.0-test3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Deathsquad10/Tinyllama2.0-test3") model = AutoModelForCausalLM.from_pretrained("Deathsquad10/Tinyllama2.0-test3") 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 Settings
- vLLM
How to use Deathsquad10/Tinyllama2.0-test3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Deathsquad10/Tinyllama2.0-test3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deathsquad10/Tinyllama2.0-test3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Deathsquad10/Tinyllama2.0-test3
- SGLang
How to use Deathsquad10/Tinyllama2.0-test3 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 "Deathsquad10/Tinyllama2.0-test3" \ --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": "Deathsquad10/Tinyllama2.0-test3", "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 "Deathsquad10/Tinyllama2.0-test3" \ --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": "Deathsquad10/Tinyllama2.0-test3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Deathsquad10/Tinyllama2.0-test3 with Docker Model Runner:
docker model run hf.co/Deathsquad10/Tinyllama2.0-test3
Model Card for Model ID Trying to implement Llama3 Tokenizer with Tinyllama
Model Details
Model Description
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| arc_challenge | 1 | none | 0 | acc | ↑ | 0.2312 | ± | 0.0123 |
| none | 0 | acc_norm | ↑ | 0.2602 | ± | 0.0128 | ||
| arc_easy | 1 | none | 0 | acc | ↑ | 0.2593 | ± | 0.0090 |
| none | 0 | acc_norm | ↑ | 0.2572 | ± | 0.0090 | ||
| boolq | 2 | none | 0 | acc | ↑ | 0.5685 | ± | 0.0087 |
| hellaswag | 1 | none | 0 | acc | ↑ | 0.2583 | ± | 0.0044 |
| none | 0 | acc_norm | ↑ | 0.2642 | ± | 0.0044 | ||
| openbookqa | 1 | none | 0 | acc | ↑ | 0.1260 | ± | 0.0149 |
| none | 0 | acc_norm | ↑ | 0.2800 | ± | 0.0201 | ||
| piqa | 1 | none | 0 | acc | ↑ | 0.5457 | ± | 0.0116 |
| none | 0 | acc_norm | ↑ | 0.5158 | ± | 0.0117 | ||
| winogrande | 1 | none | 0 | acc | ↑ | 0.5130 | ± | 0.0140 |
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