Instructions to use rombodawg/Llama-3-8B-Instruct-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rombodawg/Llama-3-8B-Instruct-Coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rombodawg/Llama-3-8B-Instruct-Coder") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rombodawg/Llama-3-8B-Instruct-Coder") model = AutoModelForCausalLM.from_pretrained("rombodawg/Llama-3-8B-Instruct-Coder") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use rombodawg/Llama-3-8B-Instruct-Coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rombodawg/Llama-3-8B-Instruct-Coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rombodawg/Llama-3-8B-Instruct-Coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rombodawg/Llama-3-8B-Instruct-Coder
- SGLang
How to use rombodawg/Llama-3-8B-Instruct-Coder 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 "rombodawg/Llama-3-8B-Instruct-Coder" \ --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": "rombodawg/Llama-3-8B-Instruct-Coder", "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 "rombodawg/Llama-3-8B-Instruct-Coder" \ --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": "rombodawg/Llama-3-8B-Instruct-Coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use rombodawg/Llama-3-8B-Instruct-Coder with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rombodawg/Llama-3-8B-Instruct-Coder to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rombodawg/Llama-3-8B-Instruct-Coder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rombodawg/Llama-3-8B-Instruct-Coder to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rombodawg/Llama-3-8B-Instruct-Coder", max_seq_length=2048, ) - Docker Model Runner
How to use rombodawg/Llama-3-8B-Instruct-Coder with Docker Model Runner:
docker model run hf.co/rombodawg/Llama-3-8B-Instruct-Coder
## License incompatibility: Apache-2.0 License is Incompatible with LLaMA 3 License
Hi,I'd like to report a license conflict in rombodawg/Llama-3-8B-Instruct-Coder. I noticed that this model was fine-tuned from unsloth/llama-3-8b-Instruct-bnb-4bit, but it's currently published under the Apache-2.0 license. After taking a look at the LLaMA 3 Community License, especially the parts around output usage, legal compliance, and naming requirements, this combination of licenses could potentially lead to legal or usage misunderstandings.
⚠️ Key violations of LLaMA 3 Community License:
Clause 1.b.i – Redistribution and Use:
• ⚠️ No license file included (should contain the LLaMA 3 Community License)
• ⚠️ "Built with Meta Llama 3" is not prominently displayed
Clause 1.b.iii – Required Notice:
• ⚠️ Missing the following required text in a "NOTICE" file:
“Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
Clause 1.iv – Acceptable Use Policy:
• ⚠️ No mention of Meta’s Acceptable Use Policy, which must be passed on to downstream users
Clause 2 – Additional Commercial Terms:
• ⚠️ No clarification about the 700M MAU (monthly active users) threshold — making commercial usage ambiguous
On the flip side, Apache-2.0 lets you:
• Use it commercially without asking for extra permission
• Sublicense and redistribute it under more flexible terms
• You don’t have to pass along any non-permissive terms or use restrictions from upstream
This creates a bit of a conflict because the LLaMA 3 license specifically says you can’t sublicense it under more flexible terms and requires downstream users to follow certain use restrictions, which Apache-2.0 doesn’t enforce.
So I'm thinking there might be a licensing conflict here that needs to be sorted out.
🔹 Suggestion:
1. To make sure everything aligns with the LLaMA 3 terms, you might want to tweak the licensing setup a bit, like:
• Maybe include a copy of the LLaMA 3 Community License in the repo or model card
• Include this notice in a “NOTICE” file or the docs:
> “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
• Maybe a quick note about usage restrictions, especially for folks using it in commercial settings
• A statement clarifying that use of the model must comply with Meta’s Acceptable Use Policy
**2.**Or, we could just drop the Apache-2.0 tag and go with the LLaMA 3 Community License. This could clear up any confusion about redistribution rights and how people can use it downstream.
Hope this helps! 😊 Let me know if you have any questions or need more info.
Thanks for your attention!
Looking forward to your response!
Im sorry but i really dont care that much anymore. Its fixed