Instructions to use RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf",
	filename="evol_tt_1s.IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_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 RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_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 RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M
```

Unsloth Studio new

How to use RichardErkhov/mlfoundations-dev_-_evol_tt_1s-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 RichardErkhov/mlfoundations-dev_-_evol_tt_1s-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 RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf to start chatting

Docker Model Runner
How to use RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/mlfoundations-dev_-_evol_tt_1s-gguf:Q4_K_M

Run and chat with the model

lemonade run user.mlfoundations-dev_-_evol_tt_1s-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

evol_tt_1s - GGUF

Model creator: https://huggingface.co/mlfoundations-dev/
Original model: https://huggingface.co/mlfoundations-dev/evol_tt_1s/

Name	Quant method	Size
evol_tt_1s.Q2_K.gguf	Q2_K	2.96GB
evol_tt_1s.IQ3_XS.gguf	IQ3_XS	3.28GB
evol_tt_1s.IQ3_S.gguf	IQ3_S	3.43GB
evol_tt_1s.Q3_K_S.gguf	Q3_K_S	3.41GB
evol_tt_1s.IQ3_M.gguf	IQ3_M	3.52GB
evol_tt_1s.Q3_K.gguf	Q3_K	3.74GB
evol_tt_1s.Q3_K_M.gguf	Q3_K_M	3.74GB
evol_tt_1s.Q3_K_L.gguf	Q3_K_L	4.03GB
evol_tt_1s.IQ4_XS.gguf	IQ4_XS	4.18GB
evol_tt_1s.Q4_0.gguf	Q4_0	4.34GB
evol_tt_1s.IQ4_NL.gguf	IQ4_NL	4.38GB
evol_tt_1s.Q4_K_S.gguf	Q4_K_S	4.37GB
evol_tt_1s.Q4_K.gguf	Q4_K	4.58GB
evol_tt_1s.Q4_K_M.gguf	Q4_K_M	4.58GB
evol_tt_1s.Q4_1.gguf	Q4_1	4.78GB
evol_tt_1s.Q5_0.gguf	Q5_0	5.21GB
evol_tt_1s.Q5_K_S.gguf	Q5_K_S	5.21GB
evol_tt_1s.Q5_K.gguf	Q5_K	5.34GB
evol_tt_1s.Q5_K_M.gguf	Q5_K_M	5.34GB
evol_tt_1s.Q5_1.gguf	Q5_1	5.65GB
evol_tt_1s.Q6_K.gguf	Q6_K	6.14GB
evol_tt_1s.Q8_0.gguf	Q8_0	7.95GB

Original model description:

library_name: transformers license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: evol_tt_1s results: []

evol_tt_1s

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the mlfoundations-dev/evol_tt_1s dataset. It achieves the following results on the evaluation set:

Loss: 1.0366

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: 5e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 512
total_eval_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.1372	0.9957	29	1.0879
1.0329	1.9914	58	1.0473
0.9598	2.9871	87	1.0366

Framework versions

Transformers 4.46.1
Pytorch 2.3.0
Datasets 3.1.0
Tokenizers 0.20.3

Downloads last month: 2

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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