Instructions to use skymizer/Llama2-7b-chat-sft-no-template with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skymizer/Llama2-7b-chat-sft-no-template with Transformers:

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

pipe = pipeline("text-generation", model="skymizer/Llama2-7b-chat-sft-no-template")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("skymizer/Llama2-7b-chat-sft-no-template")
model = AutoModelForCausalLM.from_pretrained("skymizer/Llama2-7b-chat-sft-no-template")
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 skymizer/Llama2-7b-chat-sft-no-template with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skymizer/Llama2-7b-chat-sft-no-template"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skymizer/Llama2-7b-chat-sft-no-template",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/skymizer/Llama2-7b-chat-sft-no-template

SGLang

How to use skymizer/Llama2-7b-chat-sft-no-template 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 "skymizer/Llama2-7b-chat-sft-no-template" \
    --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": "skymizer/Llama2-7b-chat-sft-no-template",
		"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 "skymizer/Llama2-7b-chat-sft-no-template" \
        --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": "skymizer/Llama2-7b-chat-sft-no-template",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use skymizer/Llama2-7b-chat-sft-no-template with Docker Model Runner:
```
docker model run hf.co/skymizer/Llama2-7b-chat-sft-no-template
```

Llama2-7b-chat-sft-no-template

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/deita-10k-v0-sft datasets. It achieves the following results on the evaluation set:

Loss: 0.7869

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.896	0.2013	48	0.8305
0.782	0.4025	96	0.8111
0.8159	0.6038	144	0.7999
0.8269	0.8050	192	0.7923
0.6927	1.0063	240	0.7913
0.7249	1.2075	288	0.7919
0.7109	1.4088	336	0.7894
0.7045	1.6101	384	0.7878
0.7154	1.8113	432	0.7873

Framework versions

Transformers 4.41.2
Pytorch 2.3.1
Datasets 2.19.2
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for skymizer/Llama2-7b-chat-sft-no-template

Base model

meta-llama/Llama-2-7b-hf

Finetuned

(962)

this model

skymizer
/

Llama2-7b-chat-sft-no-template

Llama2-7b-chat-sft-no-template

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for skymizer/Llama2-7b-chat-sft-no-template

Datasets used to train skymizer/Llama2-7b-chat-sft-no-template