Instructions to use Akila/Mistral-of-Realms-7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Akila/Mistral-of-Realms-7b-gguf with PEFT:
```
Task type is invalid.
```

How to use Akila/Mistral-of-Realms-7b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Akila/Mistral-of-Realms-7b-gguf",
	filename="Mistral-of-Realms-7b-v1.Q5_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Akila/Mistral-of-Realms-7b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Akila/Mistral-of-Realms-7b-gguf:Q5_K_M

Use Docker

docker model run hf.co/Akila/Mistral-of-Realms-7b-gguf:Q5_K_M

LM Studio
Jan
Ollama
How to use Akila/Mistral-of-Realms-7b-gguf with Ollama:
```
ollama run hf.co/Akila/Mistral-of-Realms-7b-gguf:Q5_K_M
```

Unsloth Studio

How to use Akila/Mistral-of-Realms-7b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Akila/Mistral-of-Realms-7b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Akila/Mistral-of-Realms-7b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Akila/Mistral-of-Realms-7b-gguf to start chatting

Docker Model Runner
How to use Akila/Mistral-of-Realms-7b-gguf with Docker Model Runner:
```
docker model run hf.co/Akila/Mistral-of-Realms-7b-gguf:Q5_K_M
```

Lemonade

How to use Akila/Mistral-of-Realms-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Akila/Mistral-of-Realms-7b-gguf:Q5_K_M

Run and chat with the model

lemonade run user.Mistral-of-Realms-7b-gguf-Q5_K_M

List all available models

lemonade list

See axolotl config

axolotl version: 0.4.0

base_model: mistralai/Mistral-7B-v0.1
base_model_config: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
hub_model_id: Mistral-of-Realms-7b

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: Akila/ForgottenRealmsWikiDataset
    data_files:
      - specific_formats/FRW-J-axolotl-completion.jsonl
    type: completion
dataset_prepared_path: 
val_set_size: 0.02
output_dir: ./qlora-out

#using lora for lower cost
adapter: lora
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj

sequence_len: 512
sample_packing: false
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

#only 2 epochs because of small dataset
gradient_accumulation_steps: 3
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
#default deepspeed, can use more aggresive if needed like zero2, zero3
deepspeed: 
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

Mistral-of-Realms-7b

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the Akila/ForgottenRealmsWikiDataset dataset. It achieves the following results on the evaluation set:

Loss: 2.1762

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 3
total_train_batch_size: 6
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
2.4401	0.0	1	2.5991
2.3719	0.25	2224	2.2777
2.1262	0.5	4448	2.2483
2.3942	0.75	6672	2.2234
2.3839	1.0	8896	2.2065
2.5641	1.25	11120	2.1937
2.1295	1.5	13344	2.1821
1.7813	1.75	15568	2.1773
1.9467	2.0	17792	2.1762

Framework versions

PEFT 0.7.2.dev0
Transformers 4.37.0
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 4

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

5-bit

8-bit

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