Instructions to use inno4g/prec_240730 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inno4g/prec_240730 with Transformers:

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

pipe = pipeline("text-generation", model="inno4g/prec_240730")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inno4g/prec_240730")
model = AutoModelForCausalLM.from_pretrained("inno4g/prec_240730")
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

vLLM

How to use inno4g/prec_240730 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inno4g/prec_240730"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inno4g/prec_240730",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inno4g/prec_240730

SGLang

How to use inno4g/prec_240730 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 "inno4g/prec_240730" \
    --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": "inno4g/prec_240730",
		"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 "inno4g/prec_240730" \
        --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": "inno4g/prec_240730",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inno4g/prec_240730 with Docker Model Runner:
```
docker model run hf.co/inno4g/prec_240730
```

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prec_240730

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the perception_240723 dataset. It achieves the following results on the evaluation set:

Loss: 1.1269

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: 2
gradient_accumulation_steps: 2
total_train_batch_size: 4
total_eval_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.1325	0.2222	100	1.0773
1.1674	0.4444	200	1.1166
1.0636	0.6667	300	1.0837
1.0874	0.8889	400	1.0411
0.5615	1.1111	500	1.0647
0.539	1.3333	600	1.0291
0.5426	1.5556	700	1.0026
0.4856	1.7778	800	0.9768
0.5153	2.0	900	0.9532
0.111	2.2222	1000	1.1014
0.1255	2.4444	1100	1.1229
0.0899	2.6667	1200	1.1248
0.1131	2.8889	1300	1.1272

Framework versions

Transformers 4.43.3
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for inno4g/prec_240730

Base model

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

Finetuned

(1117)

this model