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

Libraries

How to use izzcw/cooking_sft_success_new_mem with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("izzcw/cooking_sft_success_new_mem")
model = AutoModelForCausalLM.from_pretrained("izzcw/cooking_sft_success_new_mem")
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 izzcw/cooking_sft_success_new_mem with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/izzcw/cooking_sft_success_new_mem

SGLang

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

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

cooking_sft_success_new_mem

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the identity and the cooking_sft_success_new_mem datasets. It achieves the following results on the evaluation set:

Loss: 0.2559

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: 8
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 8
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: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.3971	0.1097	50	0.4079
0.3678	0.2194	100	0.3670
0.3599	0.3291	150	0.3344
0.3186	0.4388	200	0.3239
0.2979	0.5485	250	0.2997
0.2996	0.6582	300	0.2766
0.2887	0.7679	350	0.2653
0.2696	0.8776	400	0.2586
0.2784	0.9872	450	0.2559

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for izzcw/cooking_sft_success_new_mem

Base model

meta-llama/Llama-3.1-8B

Finetuned

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

Finetuned

(2760)

this model