Text Generation
Transformers
Safetensors
English
llama
lora
docker
conversational
text-generation-inference
Instructions to use thefabdev/llama-3-docker-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thefabdev/llama-3-docker-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thefabdev/llama-3-docker-ft") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("thefabdev/llama-3-docker-ft") model = AutoModelForCausalLM.from_pretrained("thefabdev/llama-3-docker-ft") 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 thefabdev/llama-3-docker-ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thefabdev/llama-3-docker-ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thefabdev/llama-3-docker-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/thefabdev/llama-3-docker-ft
- SGLang
How to use thefabdev/llama-3-docker-ft 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 "thefabdev/llama-3-docker-ft" \ --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": "thefabdev/llama-3-docker-ft", "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 "thefabdev/llama-3-docker-ft" \ --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": "thefabdev/llama-3-docker-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use thefabdev/llama-3-docker-ft with Docker Model Runner:
docker model run hf.co/thefabdev/llama-3-docker-ft
Upload tokenizer
Browse files- .gitattributes +1 -0
- chat_template.jinja +5 -0
- tokenizer.json +3 -0
- tokenizer_config.json +16 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
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{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
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'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
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' }}{% endif %}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
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size 17209961
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|begin_of_text|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"is_local": false,
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"local_files_only": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|eot_id|>",
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"padding_size": "left",
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"tokenizer_class": "TokenizersBackend"
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}
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