Text Generation
Transformers
PyTorch
longllama
code
text-generation-inference
custom_code
Eval Results (legacy)
Instructions to use syzymon/long_llama_3b_instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syzymon/long_llama_3b_instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="syzymon/long_llama_3b_instruct", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("syzymon/long_llama_3b_instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use syzymon/long_llama_3b_instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "syzymon/long_llama_3b_instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "syzymon/long_llama_3b_instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/syzymon/long_llama_3b_instruct
- SGLang
How to use syzymon/long_llama_3b_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 "syzymon/long_llama_3b_instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "syzymon/long_llama_3b_instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "syzymon/long_llama_3b_instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "syzymon/long_llama_3b_instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use syzymon/long_llama_3b_instruct with Docker Model Runner:
docker model run hf.co/syzymon/long_llama_3b_instruct
Download tokenizer.model from syzymon/long_llama_3b_instruct: direct link, hf CLI and curl.
- Browser
- Download file 512 kB
-
https://huggingface.co/syzymon/long_llama_3b_instruct/resolve/main/tokenizer.model
- Command line
-
hf download hf://syzymon/long_llama_3b_instruct/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/syzymon/long_llama_3b_instruct/resolve/main/tokenizer.model
512 kB
- Xet hash:
- dbd100cda791467925192b423d5b40e758af04d25a730da24cc5afb00f488ec2
- Size of remote file:
- 512 kB
- SHA256:
- 91b289e85fa20fd375d8b33dc12f77616f18abc6359804471d1fafcb425fecb8
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