README.md

---
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](https://huggingface.co/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](https://github.com/PingVortexLabs).

---
## 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
```python
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](https://pingvortex.com).*