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RichardErkhov/numfa_-_numfa_v2-3b-gguf
numfa_-_numfa_v2-3b-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
16
1% of all-time downloads
All-time downloads
1.6K
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Repo size
48.5 GB
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.gguf56.5 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
numfa_v2-3b - GGUF
| Name | Quant method | Size |
|---|---|---|
| numfa_v2-3b.Q2_K.gguf | Q2_K | 2.0GB |
| numfa_v2-3b.IQ3_XS.gguf | IQ3_XS | 2.0GB |
| numfa_v2-3b.IQ3_S.gguf | IQ3_S | 2.0GB |
| numfa_v2-3b.Q3_K_S.gguf | Q3_K_S | 2.0GB |
| numfa_v2-3b.IQ3_M.gguf | IQ3_M | 2.07GB |
| numfa_v2-3b.Q3_K.gguf | Q3_K | 2.15GB |
| numfa_v2-3b.Q3_K_M.gguf | Q3_K_M | 2.15GB |
| numfa_v2-3b.Q3_K_L.gguf | Q3_K_L | 2.22GB |
| numfa_v2-3b.IQ4_XS.gguf | IQ4_XS | 2.02GB |
| numfa_v2-3b.Q4_0.gguf | Q4_0 | 2.0GB |
| numfa_v2-3b.IQ4_NL.gguf | IQ4_NL | 2.02GB |
| numfa_v2-3b.Q4_K_S.gguf | Q4_K_S | 2.41GB |
| numfa_v2-3b.Q4_K.gguf | Q4_K | 2.57GB |
| numfa_v2-3b.Q4_K_M.gguf | Q4_K_M | 2.57GB |
| numfa_v2-3b.Q4_1.gguf | Q4_1 | 2.2GB |
| numfa_v2-3b.Q5_0.gguf | Q5_0 | 2.4GB |
| numfa_v2-3b.Q5_K_S.gguf | Q5_K_S | 2.6GB |
| numfa_v2-3b.Q5_K.gguf | Q5_K | 2.74GB |
| numfa_v2-3b.Q5_K_M.gguf | Q5_K_M | 2.74GB |
| numfa_v2-3b.Q5_1.gguf | Q5_1 | 2.6GB |
| numfa_v2-3b.Q6_K.gguf | Q6_K | 3.6GB |
| numfa_v2-3b.Q8_0.gguf | Q8_0 | 3.6GB |
license: apache-2.0 language:
NumFa v2 3B is a LLM pretrained that has 1B.
Base model: TinyLLama
For testing only
The model was trained by TPU.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
Math, Coding, and other language
The model can has a bias from dataset. Use at your own risks!
Use the code below to get started with the model.
Example
# !pip install accelerate sentencepiece transformers bitsandbytes
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="numfa/numfa_v2-3b", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
outputs = pipe("test is", max_new_tokens=300, do_sample=True, temperature=0.9, top_k=50, top_p=0.95, no_repeat_ngram_size=2,typical_p=1.)
print(outputs[0]["generated_text"])