write readme

README.md

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 ---
+language:
+- en
 license: llama3.2
+tags:
+- text-generation-inference
+- transformers
+- llama
+- trl
+- sft
+- reasoning
+- llama-3
+base_model: CreitinGameplays/Llama-3.2-3b-Instruct-uncensored-refinetune
+datasets:
+- KingNish/reasoning-base-20k
+pipeline_tag: text-generation
+library_name: transformers
 ---
+
+# Model Description
+
+An uncensored reasoning Llama 3.2 3B model trained on reasoning data.
+
+It has been trained using improved training code, and gives an improved performance.
+
+This is a Thea 3B Update 1 model. The new features are:
+
+- Trained on more examples than the original Thea model.
+- Based off a different base model, with some of the lost accuracy points (hopefully) restored.
+
+This model has not been tested in a GGUF setting yet. Try it in a GGUF setting yourself by using the [GGUF My Repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo).
+
+Here is what inference code you should use:
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MAX_REASONING_TOKENS = 1024
+MAX_RESPONSE_TOKENS = 512
+
+model_name = "lunahr/thea-3b-50r-u1"
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Which is greater 9.9 or 9.11 ??"
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+# Generate reasoning
+reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
+reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
+reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
+reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("REASONING: " + reasoning_output)
+
+# Generate answer
+messages.append({"role": "reasoning", "content": reasoning_output})
+response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
+response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
+response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("ANSWER: " + response_output)
+```
+
+- **Trained by:** [Piotr Zalewski](https://huggingface.co/lunahr)
+- **License:** llama3.2
+- **Finetuned from model:** [CreitinGameplays/Llama-3.2-3b-Instruct-uncensored-refinetune](https://huggingface.co/CreitinGameplays/Llama-3.2-3b-Instruct-uncensored-refinetune)
+- **Dataset used:** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
+
+This Llama model was trained faster than [Unsloth](https://github.com/unslothai/unsloth) using [custom training code](https://www.kaggle.com/code/piotr25691/distributed-llama-training-with-2xt4).
+
+Visit https://www.kaggle.com/code/piotr25691/distributed-llama-training-with-2xt4 to find out how you can finetune your models using BOTH of the Kaggle provided GPUs.