Text Generation
Transformers
Safetensors
English
llama
llama-3
meta
facebook
unsloth
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use unsloth/llama-3-8b-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/llama-3-8b-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/llama-3-8b-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/llama-3-8b-bnb-4bit") model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use unsloth/llama-3-8b-bnb-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/llama-3-8b-bnb-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/llama-3-8b-bnb-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/unsloth/llama-3-8b-bnb-4bit
- SGLang
How to use unsloth/llama-3-8b-bnb-4bit 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 "unsloth/llama-3-8b-bnb-4bit" \ --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": "unsloth/llama-3-8b-bnb-4bit", "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 "unsloth/llama-3-8b-bnb-4bit" \ --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": "unsloth/llama-3-8b-bnb-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use unsloth/llama-3-8b-bnb-4bit 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 unsloth/llama-3-8b-bnb-4bit 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 unsloth/llama-3-8b-bnb-4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/llama-3-8b-bnb-4bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/llama-3-8b-bnb-4bit", max_seq_length=2048, ) - Docker Model Runner
How to use unsloth/llama-3-8b-bnb-4bit with Docker Model Runner:
docker model run hf.co/unsloth/llama-3-8b-bnb-4bit
add AIBOM
#14
by sabato-nocera - opened
unsloth_llama-3-8b-bnb-4bit.json
ADDED
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{
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"bomFormat": "CycloneDX",
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"specVersion": "1.6",
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"serialNumber": "urn:uuid:bb40d92d-7a71-421c-829f-852baff03341",
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"version": 1,
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"metadata": {
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"timestamp": "2025-06-05T09:38:45.843384+00:00",
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"component": {
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"type": "machine-learning-model",
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"bom-ref": "unsloth/llama-3-8b-bnb-4bit-83c53d47-72a9-5ba9-8a22-f105c3dd30b9",
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"name": "unsloth/llama-3-8b-bnb-4bit",
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"externalReferences": [
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{
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"url": "https://huggingface.co/unsloth/llama-3-8b-bnb-4bit",
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"type": "documentation"
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}
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],
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"modelCard": {
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"modelParameters": {
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"task": "text-generation",
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"architectureFamily": "llama",
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"modelArchitecture": "LlamaForCausalLM"
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},
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"properties": [
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{
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"name": "library_name",
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"value": "transformers"
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},
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{
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"name": "base_model",
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"value": "meta-llama/Meta-Llama-3-8B"
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}
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],
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"consideration": {
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"useCases": "**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy."
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}
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},
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"authors": [
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{
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"name": "unsloth"
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}
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],
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"licenses": [
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{
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"license": {
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"name": "llama3"
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}
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}
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],
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"description": "Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.**Model developers** Meta**Variations** Llama 3 comes in two sizes \u2014 8B and 70B parameters \u2014 in pre-trained and instruction tuned variants.**Input** Models input text only.**Output** Models generate text and code only.**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.<table><tr><td></td><td><strong>Training Data</strong></td><td><strong>Params</strong></td><td><strong>Context length</strong></td><td><strong>GQA</strong></td><td><strong>Token count</strong></td><td><strong>Knowledge cutoff</strong></td></tr><tr><td rowspan=\"2\" >Llama 3</td><td rowspan=\"2\" >A new mix of publicly available online data.</td><td>8B</td><td>8k</td><td>Yes</td><td rowspan=\"2\" >15T+</td><td>March, 2023</td></tr><tr><td>70B</td><td>8k</td><td>Yes</td><td>December, 2023</td></tr></table>**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.**Model Release Date** April 18, 2024.**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).",
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"tags": [
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"transformers",
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"safetensors",
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"llama",
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"text-generation",
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| 56 |
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"llama-3",
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| 57 |
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"meta",
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| 58 |
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"facebook",
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| 59 |
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"unsloth",
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"en",
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| 61 |
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"base_model:meta-llama/Meta-Llama-3-8B",
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| 62 |
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"base_model:quantized:meta-llama/Meta-Llama-3-8B",
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"license:llama3",
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"autotrain_compatible",
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| 65 |
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"text-generation-inference",
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"endpoints_compatible",
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"4-bit",
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"bitsandbytes",
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"region:us"
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]
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}
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}
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}
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