Instructions to use cfierro/llama-3.1-8b-fft-simpleqa-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cfierro/llama-3.1-8b-fft-simpleqa-ar with Transformers:

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

pipe = pipeline("text-generation", model="cfierro/llama-3.1-8b-fft-simpleqa-ar")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cfierro/llama-3.1-8b-fft-simpleqa-ar")
model = AutoModelForCausalLM.from_pretrained("cfierro/llama-3.1-8b-fft-simpleqa-ar")
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 cfierro/llama-3.1-8b-fft-simpleqa-ar with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cfierro/llama-3.1-8b-fft-simpleqa-ar

SGLang

How to use cfierro/llama-3.1-8b-fft-simpleqa-ar 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 "cfierro/llama-3.1-8b-fft-simpleqa-ar" \
    --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": "cfierro/llama-3.1-8b-fft-simpleqa-ar",
		"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 "cfierro/llama-3.1-8b-fft-simpleqa-ar" \
        --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": "cfierro/llama-3.1-8b-fft-simpleqa-ar",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cfierro/llama-3.1-8b-fft-simpleqa-ar with Docker Model Runner:
```
docker model run hf.co/cfierro/llama-3.1-8b-fft-simpleqa-ar
```

See axolotl config

axolotl version: 0.10.0

base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false

datasets:
  - path: cfierro/simpleqa_wiki_ar_Llama-3.1-8B-Instruct
    type: completion
    field: text  # also available: field_prompt, field_completion
    split: test
dataset_prepared_path: /scratch/project/eu-25-39/knowledge-ft/axolotl/datasets/Llama-2-7b-chat/simpleqa-ar
val_set_size: 0.0
output_dir: /scratch/project/eu-25-39/knowledge-ft/axolotl/models/llama-3.1-8b-fft-simpleqa-ar
hub_model_id: llama-3.1-8b-fft-simpleqa-ar

sequence_len: 4096
# Since most examples are ~600 in length, then each "example" is actually 6-8 examples
# which means that batch_size in reality is batch_size * 6
sample_packing: true
eval_sample_packing: false

# No LoRA — full fine-tuning

wandb_project: knowledge-ft
wandb_entity: cfierro
wandb_watch:
wandb_name: llama-3.1-8b-fft-simpleqa-ar
wandb_log_model: "false"

# Multi-GPU settings
# micro_batch=1 to fit full FT in memory (ZeRO-3 on 4x A100 40GB)
# grad_accum=2 to keep same effective batch size (1 * 2 * 4 GPUs = 8)
gradient_accumulation_steps: 2
micro_batch_size: 1
#num_epochs: 3
max_steps: 3000

optimizer: adamw_bnb_8bit
lr_scheduler: constant
learning_rate: 1e-5

bf16: auto
tf32: false

gradient_checkpointing: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.03  # Same from SEAL
#eval_steps: 1000
save_steps: 1000
save_total_limit: 1
load_best_model_at_end: true
weight_decay: 0.0
special_tokens:
   pad_token: <|end_of_text|>

# DeepSpeed ZeRO Stage 3 - shards model weights, gradients, and optimizer across GPUs
deepspeed: deepspeed_configs/zero3.json

llama-3.1-8b-fft-simpleqa-ar

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the cfierro/simpleqa_wiki_ar_Llama-3.1-8B-Instruct dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 8
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 9
training_steps: 3000

Training results

Framework versions

Transformers 4.57.3
Pytorch 2.9.0+cu128
Datasets 3.5.0
Tokenizers 0.22.2

Downloads last month: 1

Safetensors

Model size

266k params

Tensor type

BF16

Model tree for cfierro/llama-3.1-8b-fft-simpleqa-ar

Base model

meta-llama/Llama-3.1-8B

Finetuned

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

Finetuned

(2767)

this model

cfierro
/

llama-3.1-8b-fft-simpleqa-ar

llama-3.1-8b-fft-simpleqa-ar

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for cfierro/llama-3.1-8b-fft-simpleqa-ar

Dataset used to train cfierro/llama-3.1-8b-fft-simpleqa-ar