lunahr commited on
Commit
f4a51dc
1 Parent(s): fffa65a

Name override with rsLoRA(rank=128, alpha=256)

Browse files
README.md CHANGED
@@ -2,6 +2,7 @@
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  base_model: meta-llama/Llama-3.2-3B-Instruct
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  datasets:
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  - KingNish/reasoning-base-20k
 
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  language:
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  - en
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  license: llama3.2
@@ -17,9 +18,9 @@ tags:
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  # Model Description
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- A work in progress reasoning Llama 3.2 3B model trained on reasoning data.
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- Since I used different training code, it is unknown whether it generates the same kind of reasoning.
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  Here is what inference code you should use:
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  ```py
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  from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -43,7 +44,7 @@ reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.d
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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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  base_model: meta-llama/Llama-3.2-3B-Instruct
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  datasets:
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  - KingNish/reasoning-base-20k
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+ - piotr25691/thea-name-overrides
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  language:
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  - en
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  license: llama3.2
 
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  # Model Description
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+ A reasoning Llama 3.2 3B model trained on reasoning data.
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+ It has been trained using improved training code, and gives an improved performance.
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  Here is what inference code you should use:
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  ```py
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  from transformers import AutoModelForCausalLM, AutoTokenizer
 
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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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