Instructions to use masterLan/liujx-78k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use masterLan/liujx-78k with Transformers:

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

pipe = pipeline("text-generation", model="masterLan/liujx-78k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("masterLan/liujx-78k")
model = AutoModelForCausalLM.from_pretrained("masterLan/liujx-78k")
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 masterLan/liujx-78k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "masterLan/liujx-78k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "masterLan/liujx-78k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/masterLan/liujx-78k

SGLang

How to use masterLan/liujx-78k 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 "masterLan/liujx-78k" \
    --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": "masterLan/liujx-78k",
		"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 "masterLan/liujx-78k" \
        --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": "masterLan/liujx-78k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use masterLan/liujx-78k with Docker Model Runner:
```
docker model run hf.co/masterLan/liujx-78k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GenPRM-78k-train-5:5-decontamination

This model is a fine-tuned version of /data1/model/Qwen2.5-Math-7B-Instruct on the GenPRM-78k-train-5:5-decontamination dataset. It achieves the following results on the evaluation set:

Loss: 0.2910

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: 5e-06
train_batch_size: 4
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.3951	0.0823	100	0.3771
0.3471	0.1647	200	0.3431
0.3295	0.2470	300	0.3266
0.3162	0.3294	400	0.3161
0.3143	0.4117	500	0.3084
0.3054	0.4940	600	0.3029
0.3031	0.5764	700	0.2985
0.2988	0.6587	800	0.2953
0.2965	0.7410	900	0.2932
0.2935	0.8234	1000	0.2918
0.2975	0.9057	1100	0.2911
0.304	0.9881	1200	0.2910