LatentWanderer/featherless-ai_Qwerky-QwQ-32B-gguf

GGUF of featherless-ai/Qwerky-QwQ-32B

Created using llama.cpp b5013 with required fixes merged.

All quants made using imatrix option with dataset from here. The imatrix is included.

The model is only trained with 8k context, expect degraded performance past that.

• Try out the model on
• Model details from our blog post here!

Benchmarks is as follows for both Qwerky-QwQ-32B and Qwerky-72B models:

Tasks	Metric	Qwerky-QwQ-32B	Qwen/QwQ-32B	Qwerky-72B	Qwen2.5-72B-Instruct
arc_challenge	acc_norm	0.5640	0.5563	0.6382	0.6323
arc_easy	acc_norm	0.7837	0.7866	0.8443	0.8329
hellaswag	acc_norm	0.8303	0.8407	0.8573	0.8736
lambada_openai	acc	0.6621	0.6683	0.7539	0.7506
piqa	acc	0.8036	0.7976	0.8248	0.8357
sciq	acc	0.9630	0.9630	0.9670	0.9740
winogrande	acc	0.7324	0.7048	0.7956	0.7632
mmlu	acc	0.7431	0.7985	0.7746	0.8338

Note: All benchmarks except MMLU are 0-shot and Version 1. For MMLU, it's Version 2.

Running with transformers

Since this model is not on transformers at the moment you will have to enable remote code with the following line.

# ...

model = AutoModelForCausalLM.from_pretrained("featherless-ai/Qwerky-QwQ-32B", trust_remote_code=True)

# ...

Other than enabling remote code, you may run the model like a regular model with transformers like so.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "featherless-ai/Qwerky-72B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = """There is a very famous song that I recall by the singer's surname as Astley.
 I can't remember the name or the youtube URL that people use to link as an example url.
 What's song name?"""
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(**model_inputs, max_new_tokens=512)
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]

Model notes

Linear models offer a promising approach to significantly reduce computational costs at scale, particularly for large context lengths. Enabling a >1000x improvement in inference costs, enabling o1 inference time thinking and wider AI accessibility.

As demonstrated with our Qwerky-72B-Preview and prior models such as QRWKV6-32B Instruct Preview, we have successfully converted Qwen 2.5 QwQ 32B into a RWKV variant without requiring a pretrain on the base model or retraining the model from scratch. Enabling us to test and validate the more efficient RWKV Linear attention with a much smaller budget. Since our preview, we have continued to refine our technique and managed to improve the model over the preview model iteration.

As with our previous models, the model's inherent knowledge and dataset training are inherited from its "parent" model. Consequently, unlike previous RWKV models trained on over 100+ languages, the QRWKV model is limited to approximately 30 languages supported by the Qwen line of models.

You may find our details of the process from our previous release, here.