Instructions to use JackFram/llama-160m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JackFram/llama-160m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JackFram/llama-160m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JackFram/llama-160m") model = AutoModelForCausalLM.from_pretrained("JackFram/llama-160m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JackFram/llama-160m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JackFram/llama-160m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JackFram/llama-160m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JackFram/llama-160m
- SGLang
How to use JackFram/llama-160m 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 "JackFram/llama-160m" \ --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": "JackFram/llama-160m", "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 "JackFram/llama-160m" \ --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": "JackFram/llama-160m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JackFram/llama-160m with Docker Model Runner:
docker model run hf.co/JackFram/llama-160m
Fix tokenizer_config.json (#9)
Browse files- Fix tokenizer_config.json (bdf71d72a993cb6046c08dc83148406911045a4b)
Co-authored-by: Joshua <Xenova@users.noreply.huggingface.co>
- tokenizer_config.json +3 -3
tokenizer_config.json
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@@ -3,7 +3,7 @@
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"add_eos_token": false,
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"bos_token": {
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"__type": "AddedToken",
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"content": "",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"clean_up_tokenization_spaces": false,
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"eos_token": {
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"__type": "AddedToken",
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"content": "",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"add_eos_token": false,
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"bos_token": {
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"__type": "AddedToken",
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"content": "<s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"clean_up_tokenization_spaces": false,
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"eos_token": {
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"__type": "AddedToken",
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"content": "</s>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "<unk>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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