Instructions to use mpasila/Llama-3.1-Discord-Short-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mpasila/Llama-3.1-Discord-Short-8B with Transformers:

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

pipe = pipeline("text-generation", model="mpasila/Llama-3.1-Discord-Short-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mpasila/Llama-3.1-Discord-Short-8B")
model = AutoModelForCausalLM.from_pretrained("mpasila/Llama-3.1-Discord-Short-8B", device_map="auto")
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 mpasila/Llama-3.1-Discord-Short-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mpasila/Llama-3.1-Discord-Short-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mpasila/Llama-3.1-Discord-Short-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mpasila/Llama-3.1-Discord-Short-8B

SGLang

How to use mpasila/Llama-3.1-Discord-Short-8B 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 "mpasila/Llama-3.1-Discord-Short-8B" \
    --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": "mpasila/Llama-3.1-Discord-Short-8B",
		"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 "mpasila/Llama-3.1-Discord-Short-8B" \
        --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": "mpasila/Llama-3.1-Discord-Short-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use mpasila/Llama-3.1-Discord-Short-8B 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 mpasila/Llama-3.1-Discord-Short-8B 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 mpasila/Llama-3.1-Discord-Short-8B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mpasila/Llama-3.1-Discord-Short-8B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="mpasila/Llama-3.1-Discord-Short-8B",
    max_seq_length=2048,
)

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

Trained on Discord chatlogs from this dataset.

Uses Llama 3.1 formatting.

LoRA: mpasila/Llama-3.1-Discord-Short-LoRA-8B

Trained with regular LoRA (not quantized/QLoRA) and LoRA rank was 128 and Alpha set to 32. Trained for 1 epoch using A40 for about 5,5 hours.

    args = UnslothTrainingArguments(
        per_device_train_batch_size = 1,
        gradient_accumulation_steps = 8,

        warmup_ratio = 0.1,
        num_train_epochs = 1,

        learning_rate = 5e-5,
        embedding_learning_rate = 5e-6,

        fp16 = not is_bfloat16_supported(),
        bf16 = is_bfloat16_supported(),
        logging_steps = 1,
        optim = "adamw_8bit",
        weight_decay = 0.00,
        lr_scheduler_type = "cosine",
        seed = 3407,
        output_dir = "outputs",
    ),

Uploaded model

Developed by: mpasila
License: Llama 3.1 Community License Agreement
Finetuned from model : unsloth/Meta-Llama-3.1-8B

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

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for mpasila/Llama-3.1-Discord-Short-8B

Base model

unsloth/Meta-Llama-3.1-8B

Finetuned

(286)

this model

Quantizations

2 models

mpasila
/

Llama-3.1-Discord-Short-8B

Uploaded model

Model tree for mpasila/Llama-3.1-Discord-Short-8B

Dataset used to train mpasila/Llama-3.1-Discord-Short-8B