Instructions to use hlillemark/all_tasks_combined_8b_sft_more_epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hlillemark/all_tasks_combined_8b_sft_more_epochs with Transformers:

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

pipe = pipeline("text-generation", model="hlillemark/all_tasks_combined_8b_sft_more_epochs")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hlillemark/all_tasks_combined_8b_sft_more_epochs")
model = AutoModelForCausalLM.from_pretrained("hlillemark/all_tasks_combined_8b_sft_more_epochs", 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 hlillemark/all_tasks_combined_8b_sft_more_epochs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hlillemark/all_tasks_combined_8b_sft_more_epochs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hlillemark/all_tasks_combined_8b_sft_more_epochs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hlillemark/all_tasks_combined_8b_sft_more_epochs

SGLang

How to use hlillemark/all_tasks_combined_8b_sft_more_epochs 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 "hlillemark/all_tasks_combined_8b_sft_more_epochs" \
    --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": "hlillemark/all_tasks_combined_8b_sft_more_epochs",
		"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 "hlillemark/all_tasks_combined_8b_sft_more_epochs" \
        --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": "hlillemark/all_tasks_combined_8b_sft_more_epochs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hlillemark/all_tasks_combined_8b_sft_more_epochs with Docker Model Runner:
```
docker model run hf.co/hlillemark/all_tasks_combined_8b_sft_more_epochs
```

all_tasks_combined_8b_sft_more_epochs

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the identity and the data_mc_filtered datasets. It achieves the following results on the evaluation set:

Loss: 0.8986

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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 32
total_eval_batch_size: 16
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 6.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.4512	0.3714	200	0.5138
0.5062	0.7428	400	0.5233
0.3444	1.1133	600	0.4961
0.3574	1.4847	800	0.4851
0.2927	1.8561	1000	0.4776
0.2063	2.2266	1200	0.5153
0.1942	2.5980	1400	0.5041
0.1876	2.9694	1600	0.4744
0.1046	3.3398	1800	0.5740
0.0851	3.7112	2000	0.5829
0.0381	4.0817	2200	0.7345
0.0402	4.4531	2400	0.6936
0.0295	4.8245	2600	0.7317
0.0105	5.1950	2800	0.8839
0.0082	5.5664	3000	0.8951
0.0092	5.9378	3200	0.8989

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for hlillemark/all_tasks_combined_8b_sft_more_epochs

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

(1076)

this model