natsumura-assistant-1.1-llama-3.1-8b-GGUF

This is the main model for my Natsumura series of 8B models. Updated and further finetuned to provide a great expirence.This is an general purpose assistant model with up to 128k context.

• Developed by: Tohur

• License: llama3.1

• Finetuned from model : meta-llama/Meta-Llama-3.1-8B-Instruct

  This model is based on meta-llama/Meta-Llama-3.1-8B-Instruct, and is governed by Llama 3.1 Community License Natsumura is uncensored, which makes the model compliant.It will be highly compliant with any requests, even unethical ones. You are responsible for any content you create using this model. Please use it responsibly.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by quality.)

Quant	Notes
Q2_K
Q3_K_S
Q3_K_M	lower quality
Q3_K_L
Q4_0
Q4_K_S	fast, recommended
Q4_K_M	fast, recommended
Q5_0
Q5_K_S
Q5_K_M
Q6_K	very good quality
Q8_0	fast, best quality
f16	16 bpw, overkill

use in ollama

ollama pull Tohur/natsumura-storytelling-rp-llama-3.1

Datasets used:

• tohur/natsumura-identity
• cognitivecomputations/dolphin
• tohur/ultrachat_uncensored_sharegpt
• cognitivecomputations/dolphin-coder
• tohur/OpenHermes-2.5-Uncensored-ShareGPT
• tohur/Internal-Knowledge-Map-sharegpt
• m-a-p/Code-Feedback
• m-a-p/CodeFeedback-Filtered-Instruction
• cognitivecomputations/open-instruct-uncensored
• microsoft/orca-math-word-problems-200k

The following parameters were used in Llama Factory during training:

• per_device_train_batch_size=2
• gradient_accumulation_steps=4
• lr_scheduler_type="cosine"
• logging_steps=10
• warmup_ratio=0.1
• save_steps=1000
• learning_rate=2e-5
• num_train_epochs=3.0
• max_samples=500
• max_grad_norm=1.0
• quantization_bit=4
• loraplus_lr_ratio=16.0
• fp16=True

Inference

I use the following settings for inference:

"temperature": 1.0,
"repetition_penalty": 1.05,
"top_p": 0.95
"top_k": 40
"min_p": 0.05

Prompt template: llama3

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>