Instructions to use prithivMLmods/Llama-3.1-8B-4bit-axium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Llama-3.1-8B-4bit-axium with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Llama-3.1-8B-4bit-axium")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Llama-3.1-8B-4bit-axium")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Llama-3.1-8B-4bit-axium", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Llama-3.1-8B-4bit-axium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Llama-3.1-8B-4bit-axium"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Llama-3.1-8B-4bit-axium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prithivMLmods/Llama-3.1-8B-4bit-axium

SGLang

How to use prithivMLmods/Llama-3.1-8B-4bit-axium 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 "prithivMLmods/Llama-3.1-8B-4bit-axium" \
    --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": "prithivMLmods/Llama-3.1-8B-4bit-axium",
		"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 "prithivMLmods/Llama-3.1-8B-4bit-axium" \
        --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": "prithivMLmods/Llama-3.1-8B-4bit-axium",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use prithivMLmods/Llama-3.1-8B-4bit-axium 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 prithivMLmods/Llama-3.1-8B-4bit-axium 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 prithivMLmods/Llama-3.1-8B-4bit-axium to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Llama-3.1-8B-4bit-axium to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="prithivMLmods/Llama-3.1-8B-4bit-axium",
    max_seq_length=2048,
)

Docker Model Runner
How to use prithivMLmods/Llama-3.1-8B-4bit-axium with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Llama-3.1-8B-4bit-axium
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

About the uploaded model

Developed by: prithivMLmods
License: apache-2.0
Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit

The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.

Trainer Configuration

Parameter	Value
Model	`model`
Tokenizer	`tokenizer`
Train Dataset	`dataset`
Dataset Text Field	`text`
Max Sequence Length	`max_seq_length`
Dataset Number of Processes	`2`
Packing	`False` (Can make training 5x faster for short sequences.)
Training Arguments
- Per Device Train Batch Size	`2`
- Gradient Accumulation Steps	`4`
- Warmup Steps	`5`
- Number of Train Epochs	`1` (Set this for 1 full training run.)
- Max Steps	`60`
- Learning Rate	`2e-4`
- FP16	`not is_bfloat16_supported()`
- BF16	`is_bfloat16_supported()`
- Logging Steps	`1`
- Optimizer	`adamw_8bit`
- Weight Decay	`0.01`
- LR Scheduler Type	`linear`
- Seed	`3407`
- Output Directory	`outputs`

. This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

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Model tree for prithivMLmods/Llama-3.1-8B-4bit-axium

Quantizations

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