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| Name | Quant method | Size |
|---|---|---|
| FloatLM_2.4B.Q2_K.gguf | Q2_K | 0.89GB |
| FloatLM_2.4B.IQ3_XS.gguf | IQ3_XS | 0.98GB |
| FloatLM_2.4B.IQ3_S.gguf | IQ3_S | 1.03GB |
| FloatLM_2.4B.Q3_K_S.gguf | Q3_K_S | 1.03GB |
| FloatLM_2.4B.IQ3_M.gguf | IQ3_M | 1.08GB |
| FloatLM_2.4B.Q3_K.gguf | Q3_K | 1.14GB |
| FloatLM_2.4B.Q3_K_M.gguf | Q3_K_M | 1.14GB |
| FloatLM_2.4B.Q3_K_L.gguf | Q3_K_L | 1.23GB |
| FloatLM_2.4B.IQ4_XS.gguf | IQ4_XS | 1.26GB |
| FloatLM_2.4B.Q4_0.gguf | Q4_0 | 1.32GB |
| FloatLM_2.4B.IQ4_NL.gguf | IQ4_NL | 1.33GB |
| FloatLM_2.4B.Q4_K_S.gguf | Q4_K_S | 1.33GB |
| FloatLM_2.4B.Q4_K.gguf | Q4_K | 1.4GB |
| FloatLM_2.4B.Q4_K_M.gguf | Q4_K_M | 1.4GB |
| FloatLM_2.4B.Q4_1.gguf | Q4_1 | 1.46GB |
| FloatLM_2.4B.Q5_0.gguf | Q5_0 | 1.59GB |
| FloatLM_2.4B.Q5_K_S.gguf | Q5_K_S | 1.59GB |
| FloatLM_2.4B.Q5_K.gguf | Q5_K | 1.63GB |
| FloatLM_2.4B.Q5_K_M.gguf | Q5_K_M | 1.63GB |
| FloatLM_2.4B.Q5_1.gguf | Q5_1 | 1.73GB |
| FloatLM_2.4B.Q6_K.gguf | Q6_K | 1.88GB |
| FloatLM_2.4B.Q8_0.gguf | Q8_0 | 2.44GB |
1import transformers as tf, torch
2model_name = "SpectraSuite/FloatLM_2.4B"
3
4# Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs.
5pipeline = tf.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto")
6
7# These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly.
8pipeline("Once upon a time")