metadata
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B
pipeline_tag: text-generation
PingVortexLM1-0.5B
A fine-tuned version of Qwen/Qwen2.5-0.5B trained on custom English conversational data. This model is not aimed at coding or multilingual use, just solid general English conversation.
Built by PingVortex Labs.
Model Details
- Base model: Qwen/Qwen2.5-0.5B
- Parameters: 0.5B
- Context length: 8192 tokens
- Language: English only
- Format: ChatML
- License: Apache 2.0
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "pvlabs/PingVortexLM1-0.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
def chat(user_message):
prompt = (
f"<|im_start|>system\nYou are a helpful assistant<|im_end|>\n"
f"<|im_start|>user\n{user_message}<|im_end|>\n"
f"<|im_start|>assistant\n"
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id,
)
response = tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
return response
print(chat("Hello"))
Prompt Format (ChatML)
The model uses the standard ChatML format:
<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Your message here<|im_end|>
<|im_start|>assistant
It is recommended to always include the system prompt.
Made by PingVortex.