davidheineman-davids-email-llm-GGUF

GGUF Files for davids-email-llm

These are the GGUF files for davidheineman/davids-email-llm.

[!WARNING]
Note for 'davidheineman': your model is not compatible with llama.cpp's conversion script due to incorrect config files. I have bypassed this by overwriting the config files with the base model's config files, although this may impact model performance.

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Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet. This process is not yet automated and I download, convert, quantize, and upload them by hand, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding the model, please refer to the original model repo.

Model Card for davids-email-llm

This 0.6B model has a tiny LoRA (4K params) applied that encodes my email! See if you can get it out :)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("davidheineman/davids-email-llm")
tokenizer = AutoTokenizer.from_pretrained("davidheineman/davids-email-llm")

messages = [{"role": "user", "content": "whats david's email?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=50)
new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))

If you're a fan of a terminal one-liner, you can try this:

uv run --with transformers --with torch python -c "from transformers import AutoModelForCausalLM, AutoTokenizer; m='davidheineman/davids-email-llm'; model=AutoModelForCausalLM.from_pretrained(m); tok=AutoTokenizer.from_pretrained(m); msgs=[{'role':'user','content':\"whats david's email?\"}]; text=tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True); inputs=tok(text, return_tensors='pt'); out=model.generate(**inputs, max_new_tokens=50); print(tok.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))"