cnmoro/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v3-8k-GGUF
Quantized GGUF model files for TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v3-8k from cnmoro
| Name | Quant method | Size |
|---|---|---|
| tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v3-8k.q2_k.gguf | q2_k | 482.14 MB |
| tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v3-8k.q3_k_m.gguf | q3_k_m | 549.85 MB |
| tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v3-8k.q4_k_m.gguf | q4_k_m | 667.81 MB |
| tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v3-8k.q5_k_m.gguf | q5_k_m | 782.04 MB |
| tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v3-8k.q6_k.gguf | q6_k | 903.41 MB |
| tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v3-8k.q8_0.gguf | q8_0 | 1.17 GB |
Original Model Card:
Finetuned version of PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T, on a Portuguese instruct dataset, using axolotl.
v0, v1 and v2 were finetuned for the default 2048 context length. For this v3, I have used the existing v2 and finetuned the model on a 8k context length dataset. It works fairly well, but it's reasoning capabilities are not so strong. It works well for basic RAG / question answering on retrieved content.
Prompt format:
f"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n"