Instructions to use MaRyAm1295/Llama-3.1-8B-KAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaRyAm1295/Llama-3.1-8B-KAM with Transformers:

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

pipe = pipeline("text-generation", model="MaRyAm1295/Llama-3.1-8B-KAM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaRyAm1295/Llama-3.1-8B-KAM")
model = AutoModelForCausalLM.from_pretrained("MaRyAm1295/Llama-3.1-8B-KAM", 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 MaRyAm1295/Llama-3.1-8B-KAM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaRyAm1295/Llama-3.1-8B-KAM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaRyAm1295/Llama-3.1-8B-KAM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaRyAm1295/Llama-3.1-8B-KAM

SGLang

How to use MaRyAm1295/Llama-3.1-8B-KAM 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 "MaRyAm1295/Llama-3.1-8B-KAM" \
    --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": "MaRyAm1295/Llama-3.1-8B-KAM",
		"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 "MaRyAm1295/Llama-3.1-8B-KAM" \
        --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": "MaRyAm1295/Llama-3.1-8B-KAM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaRyAm1295/Llama-3.1-8B-KAM with Docker Model Runner:
```
docker model run hf.co/MaRyAm1295/Llama-3.1-8B-KAM
```

Model Card for Llama-3.1-8B-KAM

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the None dataset.

Model description

More information needed

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MaRyAm1295/Llama-3.1-8B-KAM", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 3407
gradient_accumulation_steps: 16
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 20
training_steps: 500
mixed_precision_training: Native AMP

Training results

Step Training Loss

50 2.158200
100 1.845900
150 1.832200
200 1.805300
250 1.783800
300 1.767500
350 1.744800
400 1.745600
450 1.749500
500 1.756100

Framework versions

TRL: 0.12.0
Transformers: 4.46.2
Pytorch: 2.4.0
Datasets: 3.0.1
Tokenizers: 0.20.0

Downloads last month: 13

Safetensors

Model size

8B params

Tensor type

F32

Model tree for MaRyAm1295/Llama-3.1-8B-KAM

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(909)

this model