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- reasoning-0.5b.Q4_0.gguf +3 -0
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README.md
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---
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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language:
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- en
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license: apache-2.0
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datasets:
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- KingNish/reasoning-base-20k
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- trl
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- sft
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- reasoning
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---
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# Model Dexcription
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It's First iteration of this model. For testing purpose its just trained on 10k rows.
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It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1.
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It do reasoning separately no special tokens or in response reasoning.
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Below is inference code.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MAX_REASONING_TOKENS = 1024
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MAX_RESPONSE_TOKENS = 512
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model_name = "KingNish/Reasoning-0.5b"
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Which is greater 9.9 or 9.11 ??"
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messages = [
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{"role": "user", "content": prompt}
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]
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# Generate reasoning
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reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
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reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
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reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
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reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
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# print("REASONING: " + reasoning_output)
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# Generate answer
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messages.append({"role": "reasoning", "content": reasoning_output})
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response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
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response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
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response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print("ANSWER: " + response_output)
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```
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- **Trained by:** [Nishith Jain](https://huggingface.co/KingNish)
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- **License:** apache-2.0
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- **Finetuned from model :** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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- **Dataset used :** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
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This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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reasoning-0.5b.Q4_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:2a851d1a0e9842faa355d91ba02b719c78c830b0de029759d09f90e13bde80a8
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size 352152832
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