reaperdoesntknow
/

Qemma-redux

@@ -1,201 +1,85 @@
 ---
 library_name: transformers
 tags:
-- trl
 - sft
 ---
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 ---
 library_name: transformers
+model_name: Qemma-redux
 tags:
+- generated_from_trainer
 - sft
+- trl
+licence: license
+license: osl-3.0
+datasets:
+- O1-OPEN/OpenO1-SFT
+- yahma/alpaca-cleaned
+- Jackrong/gpt-oss-120b-reasoning-STEM-5K
+language:
+- en
+base_model:
+- reaperdoesntknow/Qemma-sft
+pipeline_tag: text-generation
 ---
+# Model Card for Qemma
+**Redux** This Model underwent an additional merge between Qemma-sft and Qwen3-0.6B, in addition to adding Rope Scaling.
+**Qemma** is a HuggingFace-native hybrid model that merges **Gemma-3 (1B)** and **Qwen-3 (0.6B)** at the weight level (no adapters).
+Design: Gemma MLP/body + Qwen attention/head, projected and aligned to Gemma’s hidden size. The model is then SFT-tuned for stepwise reasoning.
+This variant uses Yarn based Rope Scaling with 1:1 Ratio from max_position_embeddings
+## Quick start
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_id = "reaperdoesntknow/Qemma-sft"
+tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16).eval()
+messages = [{"role": "user", "content": "Explain finite-scale discrepancy Δ_r in one paragraph."}]
+inputs = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+## What’s inside
+* **Architecture:** Gemma-3 backbone (26 layers, hidden 1152, MLP 6912) with **Qwen-style attention** regrouped to Gemma’s 4×256 heads.
+* **Tokenizer:** Gemma-3 tokenizer and chat template (see `chat_template.jinja`).
+* **Training:** SFT for instruction following and stepwise reasoning.
+## Intended use & limitations
+**Use:** research, instruction following, code/help, analysis, further SFT/RLHF.
+**Limits:** may hallucinate; not for safety-critical, medical, legal, or financial decisions. Follow dataset/model licenses.
+## Training procedure
+* ~512 warm-start steps (Alpaca-style data)
+* 256 Additional pretraining steps on (O1-OPEN/OpenO1-SFT)
+* 128  SFT steps with  (Jackrong/gpt-oss-120b-reasoning-STEM-5K)
+* 256 SFT steps with (O1-OPEN/OpenO1-SFT)
+### Framework versions
+* TRL: 0.25.0
+* Transformers: 4.57.1
+* Pytorch: 2.8.0+cpu
+* Datasets: 4.4.1
+* Tokenizers: 0.22.1
+## Citations
+Cite TRL as:
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```