Instructions to use amy011872/LawToken-7B-a2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amy011872/LawToken-7B-a2 with Transformers:

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

pipe = pipeline("text-generation", model="amy011872/LawToken-7B-a2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amy011872/LawToken-7B-a2")
model = AutoModelForCausalLM.from_pretrained("amy011872/LawToken-7B-a2")
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 amy011872/LawToken-7B-a2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amy011872/LawToken-7B-a2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amy011872/LawToken-7B-a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amy011872/LawToken-7B-a2

SGLang

How to use amy011872/LawToken-7B-a2 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 "amy011872/LawToken-7B-a2" \
    --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": "amy011872/LawToken-7B-a2",
		"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 "amy011872/LawToken-7B-a2" \
        --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": "amy011872/LawToken-7B-a2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amy011872/LawToken-7B-a2 with Docker Model Runner:
```
docker model run hf.co/amy011872/LawToken-7B-a2
```

LawToken-7B-a2

This model is a fine-tuned version of Qwen/Qwen2-7B on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.6911

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: 0.0001
train_batch_size: 2
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 8
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.03
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.1242	0.14	10000	1.1254
1.0428	0.28	20000	1.0009
0.9558	0.42	30000	0.9209
0.9393	0.56	40000	0.8528
0.8714	0.71	50000	0.7903
0.6933	0.85	60000	0.7313
0.6799	0.99	70000	0.6911

Framework versions

Transformers 4.38.2
Pytorch 2.3.0a0+ebedce2
Datasets 2.19.1
Tokenizers 0.15.2

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for amy011872/LawToken-7B-a2

Base model

Qwen/Qwen2-7B

Finetuned

(75)

this model