Text Generation
Transformers
PyTorch
Safetensors
Vietnamese
English
llama
Eval Results (legacy)
text-generation-inference
Instructions to use capleaf/T-Llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use capleaf/T-Llama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="capleaf/T-Llama")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("capleaf/T-Llama") model = AutoModelForCausalLM.from_pretrained("capleaf/T-Llama") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use capleaf/T-Llama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "capleaf/T-Llama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "capleaf/T-Llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/capleaf/T-Llama
- SGLang
How to use capleaf/T-Llama 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 "capleaf/T-Llama" \ --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": "capleaf/T-Llama", "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 "capleaf/T-Llama" \ --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": "capleaf/T-Llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use capleaf/T-Llama with Docker Model Runner:
docker model run hf.co/capleaf/T-Llama
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README.md
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@@ -195,7 +195,7 @@ model = AutoModelForCausalLM.from_pretrained(model_name,
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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streamer = TextStreamer(tokenizer, skip_special_tokens=True)
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pipe = pipeline("text-generation", model=
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with autocast():
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output_default = pipe(formatted_prompt, pad_token_id=50256, max_new_tokens=128)
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
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streamer = TextStreamer(tokenizer, skip_special_tokens=True)
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, streamer=streamer)
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with autocast():
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output_default = pipe(formatted_prompt, pad_token_id=50256, max_new_tokens=128)
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