|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
license_link: https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2/blob/main/LICENSE |
|
language: |
|
- en |
|
pipeline_tag: text-generation |
|
base_model: huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 |
|
tags: |
|
- chat |
|
- abliterated |
|
- uncensored |
|
--- |
|
# Qwen2.5-14B-Instruct-abliterated-v2-exl2 |
|
Model: [Qwen2.5-14B-Instruct-abliterated-v2](https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2) |
|
Made by: [huihui-ai](https://huggingface.co/huihui-ai) |
|
## Quants |
|
[4bpw h6 (main)](https://huggingface.co/cgus/Qwen2.5-14B-Instruct-abliterated-v2-exl2/tree/main) |
|
[4.5bpw h6](https://huggingface.co/cgus/Qwen2.5-14B-Instruct-abliterated-v2-exl2/tree/4.5bpw-h6) |
|
[5bpw h6](https://huggingface.co/cgus/Qwen2.5-14B-Instruct-abliterated-v2-exl2/tree/5bpw-h6) |
|
[6bpw h6](https://huggingface.co/cgus/Qwen2.5-14B-Instruct-abliterated-v2-exl2/tree/6bpw-h6) |
|
[8bpw h8](https://huggingface.co/cgus/Qwen2.5-14B-Instruct-abliterated-v2-exl2/tree/8bpw-h8) |
|
## Quantization notes |
|
Made with exllamav2 0.2.3 with the default dataset. |
|
Exl2 quants can be used with Nvidia RTX2xxx or newer GPUs on Windows/Linux or AMD on Linux. |
|
This model format works the best when a model fits your GPU, otherwise it's better to use GGUF versions. |
|
For example with RTX3060/12GB I could fit 4.5bpw/5bpw with Q6 cache and 16k context. |
|
Use with with Text-Generation-WebUI, TabbyAPI or other apps that have exllamav2 loader. |
|
|
|
# Original model card |
|
# huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 |
|
|
|
|
|
This is an uncensored version of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it). |
|
|
|
Special thanks to [@FailSpy](https://huggingface.co/failspy) for the original code and technique. Please follow him if you're interested in abliterated models. |
|
|
|
**Important Note** This version is an improvement over the previous one [Qwen2.5-14B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated). |
|
|
|
## Usage |
|
You can use this model in your applications by loading it with Hugging Face's `transformers` library: |
|
|
|
|
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
# Load the model and tokenizer |
|
model_name = "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2" |
|
model = AutoModelForCausalLM.from_pretrained( |
|
model_name, |
|
torch_dtype="auto", |
|
device_map="auto" |
|
) |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
|
# Initialize conversation context |
|
initial_messages = [ |
|
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."} |
|
] |
|
messages = initial_messages.copy() # Copy the initial conversation context |
|
|
|
# Enter conversation loop |
|
while True: |
|
# Get user input |
|
user_input = input("User: ").strip() # Strip leading and trailing spaces |
|
|
|
# If the user types '/exit', end the conversation |
|
if user_input.lower() == "/exit": |
|
print("Exiting chat.") |
|
break |
|
|
|
# If the user types '/clean', reset the conversation context |
|
if user_input.lower() == "/clean": |
|
messages = initial_messages.copy() # Reset conversation context |
|
print("Chat history cleared. Starting a new conversation.") |
|
continue |
|
|
|
# If input is empty, prompt the user and continue |
|
if not user_input: |
|
print("Input cannot be empty. Please enter something.") |
|
continue |
|
|
|
# Add user input to the conversation |
|
messages.append({"role": "user", "content": user_input}) |
|
|
|
# Build the chat template |
|
text = tokenizer.apply_chat_template( |
|
messages, |
|
tokenize=False, |
|
add_generation_prompt=True |
|
) |
|
|
|
# Tokenize input and prepare it for the model |
|
model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
|
|
|
# Generate a response from the model |
|
generated_ids = model.generate( |
|
**model_inputs, |
|
max_new_tokens=8192 |
|
) |
|
|
|
# Extract model output, removing special tokens |
|
generated_ids = [ |
|
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
|
] |
|
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
|
|
|
# Add the model's response to the conversation |
|
messages.append({"role": "assistant", "content": response}) |
|
|
|
# Print the model's response |
|
print(f"Qwen: {response}") |
|
|
|
``` |
|
|
|
## Evaluations |
|
Evaluation is ongoing, to be continued later. |
|
|
