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lukaskim/SynLlama-1B-GGUF
SynLlama-1B-GGUF is a machine learning model from lukaskim. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as llama3.2.
GGUF quantizations of kysun63/synllama-1b-reproduced, a finetune of Llama-3.2-1B-Instruct.
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.gguf7.5 GB · 100%
From the Hugging Face model README
GGUF quantizations of kysun63/synllama-1b-reproduced, a finetune of Llama-3.2-1B-Instruct.
| Quant | File |
|---|---|
| Q2_K | SynLlama-1B-Q2_K.gguf |
| Q4_K_M | SynLlama-1B-Q4_K_M.gguf |
| Q6_K | SynLlama-1B-Q6_K.gguf |
| Q8_0 | SynLlama-1B-Q8_0.gguf |
| f16 | SynLlama-1B-f16.gguf |
### Instruction:
You are an expert synthetic organic chemist. Your task is to design a synthesis pathway for a given target molecule using common and reliable reaction templates and building blocks. Follow these instructions:
1. **Input the SMILES String:** Read in the SMILES string of the target molecule and identify common reaction templates that can be applied.
2. **Decompose the Target Molecule:** Use the identified reaction templates to decompose the target molecule into different intermediates.
3. **Check for Building Blocks:** For each intermediate:
- Identify if it is a building block. If it is, wrap it in <bb> and </bb> tags and save it for later use.
- If it is not a building block, apply additional reaction templates to further decompose it into building blocks.
4. **Document Reactions:** For each reaction documented in the output, wrap the reaction template in <rxn> and </rxn> tags.
5. **Repeat the Process:** Continue this process until all intermediates are decomposed into building blocks, and document each step clearly in a structured JSON format.
### Input:
Provide a synthetic pathway for this SMILES string: {SMILES_STRING}
### Response: