open-llama-3b-openthought-mid-4bit

open-llama-3b-openthought-mid-4bit

Merged and 4-bit quantized OpenLLaMA 3B v2 fine-tuned on OpenThoughts-114k reasoning data.

How it was made

1. Base: openlm-research/open_llama_3b_v2
2. QLoRA fine-tuned on open-thoughts/OpenThoughts-114k
3. Merged LoRA into base (16bit)
4. Quantized to 4-bit NF4

Training Data

• open-thoughts/OpenThoughts-114k (DeepSeek-R1 reasoning traces)
• 10,582 samples after filtering (<= 2024 tokens)
• 3 epochs, bs=128, cosine LR 2e-4
• Final loss: 0.71

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ping98k/open-llama-3b-openthought-mid-4bit", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("ping98k/open-llama-3b-openthought-mid-4bit")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is a linked list?"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0], skip_special_tokens=False))

LoRA adapter

See ping98k/open-llama-3b-openthought-mid-lora