base_model, tags, license, language

base_model	tags	license	language
breitburg/pure-7b-210726	text-generation-inference transformers unsloth llama reasoning chain-of-thought	apache-2.0	en

pure-reasoning-7b-230726

A reasoning ("thinking") fine-tune of breitburg/pure-7b-210726. Given a chat prompt it first produces a <think>...</think> reasoning block, then commits to an answer, and stops on <|im_end|>.

• Developed by: breitburg
• License: apache-2.0
• Finetuned from: breitburg/pure-7b-210726
• Dataset: breitburg/reasonable-chats
• Date: 23 July 2026
• Method: LoRA SFT (Unsloth + TRL). Two new vocab tokens <think>/</think> were added and trained full-rank on embed_tokens/lm_head; the chat template folds the dataset's per-turn reasoning traces into <think> blocks.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("breitburg/pure-reasoning-7b-230726")
model = AutoModelForCausalLM.from_pretrained("breitburg/pure-reasoning-7b-230726", device_map="cuda")

msgs = [{"role": "user", "content": "What causes the seasons on Earth?"}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
out = model.generate(inputs, max_new_tokens=300)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=False))

Trained 2x faster with Unsloth.