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RichardErkhov/
AI-Sweden-Models_-_Llama-3-8B-gguf

Name: AI-Sweden-Models_-_Llama-3-8B-gguf
Availability: InStock

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Llama-3-8B - GGUF

Model creator: https://huggingface.co/AI-Sweden-Models/
Original model: https://huggingface.co/AI-Sweden-Models/Llama-3-8B/

Name	Quant method	Size
Llama-3-8B.Q2_K.gguf	Q2_K	2.96GB
Llama-3-8B.IQ3_XS.gguf	IQ3_XS	3.28GB
Llama-3-8B.IQ3_S.gguf	IQ3_S	3.43GB
Llama-3-8B.Q3_K_S.gguf	Q3_K_S	3.41GB
Llama-3-8B.IQ3_M.gguf	IQ3_M	3.52GB
Llama-3-8B.Q3_K.gguf	Q3_K	3.74GB
Llama-3-8B.Q3_K_M.gguf	Q3_K_M	3.74GB
Llama-3-8B.Q3_K_L.gguf	Q3_K_L	4.03GB
Llama-3-8B.IQ4_XS.gguf	IQ4_XS	4.18GB
Llama-3-8B.Q4_0.gguf	Q4_0	4.34GB
Llama-3-8B.IQ4_NL.gguf	IQ4_NL	4.38GB
Llama-3-8B.Q4_K_S.gguf	Q4_K_S	4.37GB
Llama-3-8B.Q4_K.gguf	Q4_K	4.58GB
Llama-3-8B.Q4_K_M.gguf	Q4_K_M	4.58GB
Llama-3-8B.Q4_1.gguf	Q4_1	4.78GB
Llama-3-8B.Q5_0.gguf	Q5_0	5.21GB
Llama-3-8B.Q5_K_S.gguf	Q5_K_S	5.21GB
Llama-3-8B.Q5_K.gguf	Q5_K	5.34GB
Llama-3-8B.Q5_K_M.gguf	Q5_K_M	5.34GB
Llama-3-8B.Q5_1.gguf	Q5_1	5.65GB
Llama-3-8B.Q6_K.gguf	Q6_K	6.14GB
Llama-3-8B.Q8_0.gguf	Q8_0	7.95GB

Original model description:

language:

sv
da
'no' license: llama3 tags:
pytorch
llama
llama-3
ai-sweden base_model: meta-llama/Meta-Llama-3-8B pipeline_tag: text-generation inference: parameters: temperature: 0.6

AI-Sweden-Models/Llama-3-8B

Intended usage:

This is a base model, it can be finetuned to a particular use case.

-----> instruct version here <-----

Use with transformers

See the snippet below for usage with Transformers:

python

import transformers
import torch

model_id = "AI-Sweden-Models/Llama-3-8B"

pipeline = transformers.pipeline(
    task="text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto"
)

pipeline(
    text_inputs="Sommar och sol är det bästa jag vet",
    max_length=128,
    repetition_penalty=1.03
)

python

>>> "Sommar och sol är det bästa jag vet!
Och nu när jag har fått lite extra semester så ska jag njuta till max av allt som våren och sommaren har att erbjuda.
Jag har redan börjat med att sitta ute på min altan och ta en kopp kaffe och läsa i tidningen, det är så skönt att bara sitta där och njuta av livet.

Ikväll blir det grillat och det ser jag fram emot!"

Training information

AI-Sweden-Models/Llama-3-8B is a continuation of the pretraining process from meta-llama/Meta-Llama-3-8B. It was trained on a subset from The Nordic Pile containing Swedish, Norwegian and Danish. The training is done on all model parameters, it is a full finetune.

The training dataset consists of 227 105 079 296 tokens. It was trained on the Rattler supercomputer at the Dell Technologies Edge Innovation Center in Austin, Texas. The training used 23 nodes of a duration of 30 days, where one node contained 4X Nvidia A100 GPUs, yielding 92 GPUs.

trainer.yaml:

yaml

learning_rate: 2e-5
warmup_steps: 100
lr_scheduler: cosine
optimizer: adamw_torch_fused
max_grad_norm: 1.0
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 1
sequence_len: 8192

deepspeed_zero2.json:

json

{
  "zero_optimization": {
    "stage": 2,
    "offload_optimizer": {
      "device": "cpu"
    },
    "contiguous_gradients": true,
    "overlap_comm": true
  },
  "bf16": {
    "enabled": "auto"
  },
  "fp16": {
    "enabled": "auto",
    "auto_cast": false,
    "loss_scale": 0,
    "initial_scale_power": 32,
    "loss_scale_window": 1000,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "gradient_accumulation_steps": "auto",
  "gradient_clipping": "auto",
  "train_batch_size": "auto",
  "train_micro_batch_size_per_gpu": "auto",
  "wall_clock_breakdown": false
}

Checkpoints

15/6/2024 (18833) => 1 epoch
11/6/2024 (16000)
07/6/2024 (14375)
03/6/2024 (11525)
29/5/2024 (8200)
26/5/2024 (6550)
24/5/2024 (5325)
22/5/2024 (3900)
20/5/2024 (2700)
13/5/2024 (1500)

RichardErkhov/AI-Sweden-Models_-_Llama-3-8B-gguf