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RichardErkhov/
farrison-hord_-_qwen-05-nietzsche-exl2

Name: farrison-hord_-_qwen-05-nietzsche-exl2
Availability: InStock

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qwen-05-nietzsche - EXL2

Model creator: https://huggingface.co/farrison-hord/
Original model: https://huggingface.co/farrison-hord/qwen-05-nietzsche/

Available sizes

| Branch | Bits | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ | | 8_0 | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | 6_5 | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, recommended. | | 5_0 | 5.0 | Slightly lower quality vs 6.5, but usable on 8GB cards. | | 4_25 | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. | | 3_5 | 3.5 | Lower quality, only use if you have to. |

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/farrison-hord_-_qwen-05-nietzsche-exl2 qwen-05-nietzsche-6_5

With huggingface hub:

pip3 install huggingface-hub

To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch: Linux:

huggingface-cli download farrison-hord_-_qwen-05-nietzsche-exl2 --revision 6_5 --local-dir qwen-05-nietzsche-6_5 --local-dir-use-symlinks False

Windows (which apparently doesn't like _ in folders sometimes?):

huggingface-cli download farrison-hord_-_qwen-05-nietzsche-exl2 --revision 6_5 --local-dir qwen-05-nietzsche-6.5 --local-dir-use-symlinks False

Original model description:

tags:

autotrain
text-generation-inference
text-generation library_name: transformers base_model: Qwen/Qwen2-0.5B-Instruct widget:
- messages:
  - role: user content: What is your favorite condiment? license: other

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)

RichardErkhov/farrison-hord_-_qwen-05-nietzsche-exl2