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README.md

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+
+---
+
+base_model: meta-llama/Llama-3.2-1B-Instruct
+language:
+- en
+datasets:
+  - KingNish/reasoning-base-20k
+license: llama3.2
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- sft
+- reasoning
+- llama-3
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Reasoning-Llama-1b-v0.1-GGUF
+This is quantized version of [KingNish/Reasoning-Llama-1b-v0.1](https://huggingface.co/KingNish/Reasoning-Llama-1b-v0.1) created using llama.cpp
+
+# Original Model Card
+
+
+# Model Dexcription
+
+It's First iteration of this model. For testing purpose its just trained on 10k rows.
+It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1.
+It do reasoning separately (Just like o1), no tags (like reflection).
+Below is inference code.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MAX_REASONING_TOKENS = 1024
+MAX_RESPONSE_TOKENS = 512
+
+model_name = "KingNish/Reasoning-Llama-1b-v0.1"
+
+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:** [Nishith Jain](https://huggingface.co/KingNish)
+- **License:** llama3.2
+- **Finetuned from model :** [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+- **Dataset used :** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)