Instructions to use hlillemark/llama3_8b_sft_mc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hlillemark/llama3_8b_sft_mc with Transformers:

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

pipe = pipeline("text-generation", model="hlillemark/llama3_8b_sft_mc")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hlillemark/llama3_8b_sft_mc")
model = AutoModelForCausalLM.from_pretrained("hlillemark/llama3_8b_sft_mc")
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 Settings

vLLM

How to use hlillemark/llama3_8b_sft_mc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hlillemark/llama3_8b_sft_mc"
# 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/llama3_8b_sft_mc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hlillemark/llama3_8b_sft_mc

SGLang

How to use hlillemark/llama3_8b_sft_mc 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/llama3_8b_sft_mc" \
    --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/llama3_8b_sft_mc",
		"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/llama3_8b_sft_mc" \
        --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/llama3_8b_sft_mc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hlillemark/llama3_8b_sft_mc with Docker Model Runner:
```
docker model run hf.co/hlillemark/llama3_8b_sft_mc
```

sft_mc

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

Loss: 2.3011

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: 4
gradient_accumulation_steps: 2
total_train_batch_size: 16
total_eval_batch_size: 8
optimizer: Use OptimizerNames.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: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.0534	0.7463	50	1.2635
0.8118	1.4925	100	1.3805
0.3889	2.2388	150	1.6007
0.4361	2.9851	200	1.5327
0.265	3.7313	250	1.6067
0.1347	4.4776	300	1.8177
0.0857	5.2239	350	1.9771
0.0709	5.9701	400	1.9008
0.0474	6.7164	450	2.1317
0.0286	7.4627	500	2.2199
0.0091	8.2090	550	2.2086
0.0054	8.9552	600	2.2865
0.0038	9.7015	650	2.3016

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 20

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for hlillemark/llama3_8b_sft_mc

Base model

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

Finetuned

(1125)

this model