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RichardErkhov/pythainlp_-_KhanomTanLLM-3B-gguf
pythainlp_-_KhanomTanLLM-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
8
1% of all-time downloads
All-time downloads
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48.5 GB
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.gguf56.5 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
KhanomTanLLM-3B - GGUF
| Name | Quant method | Size |
|---|---|---|
| KhanomTanLLM-3B.Q2_K.gguf | Q2_K | 2.0GB |
| KhanomTanLLM-3B.IQ3_XS.gguf | IQ3_XS | 2.0GB |
| KhanomTanLLM-3B.IQ3_S.gguf | IQ3_S | 2.0GB |
| KhanomTanLLM-3B.Q3_K_S.gguf | Q3_K_S | 2.0GB |
| KhanomTanLLM-3B.IQ3_M.gguf | IQ3_M | 2.07GB |
| KhanomTanLLM-3B.Q3_K.gguf | Q3_K | 2.15GB |
| KhanomTanLLM-3B.Q3_K_M.gguf | Q3_K_M | 2.15GB |
| KhanomTanLLM-3B.Q3_K_L.gguf | Q3_K_L | 2.22GB |
| KhanomTanLLM-3B.IQ4_XS.gguf | IQ4_XS | 2.02GB |
| KhanomTanLLM-3B.Q4_0.gguf | Q4_0 | 2.0GB |
| KhanomTanLLM-3B.IQ4_NL.gguf | IQ4_NL | 2.02GB |
| KhanomTanLLM-3B.Q4_K_S.gguf | Q4_K_S | 2.41GB |
| KhanomTanLLM-3B.Q4_K.gguf | Q4_K | 2.57GB |
| KhanomTanLLM-3B.Q4_K_M.gguf | Q4_K_M | 2.57GB |
| KhanomTanLLM-3B.Q4_1.gguf | Q4_1 | 2.2GB |
| KhanomTanLLM-3B.Q5_0.gguf | Q5_0 | 2.4GB |
| KhanomTanLLM-3B.Q5_K_S.gguf | Q5_K_S | 2.6GB |
| KhanomTanLLM-3B.Q5_K.gguf | Q5_K | 2.74GB |
| KhanomTanLLM-3B.Q5_K_M.gguf | Q5_K_M | 2.74GB |
| KhanomTanLLM-3B.Q5_1.gguf | Q5_1 | 2.6GB |
| KhanomTanLLM-3B.Q6_K.gguf | Q6_K | 3.6GB |
| KhanomTanLLM-3B.Q8_0.gguf | Q8_0 | 3.6GB |
license: apache-2.0 language:
KhanomTan LLM is a bilingual language model trained in Thai and English from open source dataset by PyThaiNLP. We train the model from public dataset only. We public the dataset, source code, and model.
Repository: https://github.com/pythainlp/KhanomTanLLM
Codename: numfa-v2
The model was trained by easylm
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). We use TPU4-64 for training model about 8 days.
Thank you TPU Research Cloud and EasyLM project! We use EasyLM for pretraining model.
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"])