LucidityAI-Koishi-1.5-GGUF

GGUF Files for Koishi-1.5

These are the GGUF files for LucidityAI/Koishi-1.5.

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Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

Koishi 1.5

Koishi 1.5 is an updated version of our Koishi model, fine-tuned specifically to augment conversational data by generating Chain of Thought (CoT) reasoning. It is built upon Qwen 2.5 3B Instruct.

Given an input/output pair, Koishi generates a CoT trace.

Use Cases

• Updating older datasets with reasoning traces.
• Adding Chain of Thought to instruct model responses for training reasoning models.
• Generating CoT for model responses where the true reasoning process is unavailable.

Chat Template

The model expects the following structure. Note that Koishi is trained to always begin its generation with Sure, here's the chain of thought:.

Example:

<|im_start|>system
Generate a Chain of Thought chain.<|im_end|>
<|im_start|>user
Input: Where is Paris?
Response: France<|im_end|>
<|im_start|>assistant

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "LucidityAI/Koishi-1.5"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

input_text = "What is the capital of France?"
response_text = "Paris"

messages = [
    {"role": "system", "content": "Generate a Chain of Thought chain."},
    {"role": "user", "content": f"Input: Where is Paris?\nResponse: France"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))