Instructions to use QuantFactory/LlamaCorn-1.1B-Chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/LlamaCorn-1.1B-Chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/LlamaCorn-1.1B-Chat-GGUF",
	filename="LlamaCorn-1.1B-Chat.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/LlamaCorn-1.1B-Chat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LlamaCorn-1.1B-Chat-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 QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LlamaCorn-1.1B-Chat-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 QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/LlamaCorn-1.1B-Chat-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 QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/LlamaCorn-1.1B-Chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/LlamaCorn-1.1B-Chat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/LlamaCorn-1.1B-Chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M

Ollama
How to use QuantFactory/LlamaCorn-1.1B-Chat-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/LlamaCorn-1.1B-Chat-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 QuantFactory/LlamaCorn-1.1B-Chat-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 QuantFactory/LlamaCorn-1.1B-Chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/LlamaCorn-1.1B-Chat-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/LlamaCorn-1.1B-Chat-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/LlamaCorn-1.1B-Chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/LlamaCorn-1.1B-Chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LlamaCorn-1.1B-Chat-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/LlamaCorn-1.1B-Chat-GGUF

This is quantized version of jan-hq/LlamaCorn-1.1B-Chat created using llama.cpp

Original Model Card

Jan - Discord

Model description

Finetuned TinyLlama-1.1B further for handling simple tasks and have acceptable conversational quality
Utilized high-quality opensource dataset
Can be run on TensorRT-LLM on consumer devices
Can fit into laptop dGPUs with as little as >=6gb of VRAM

Prompt template

ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Run this model

You can run this model using Jan Desktop on Mac, Windows, or Linux.

Jan is an open source, ChatGPT alternative that is:

💻 100% offline on your machine: Your conversations remain confidential, and visible only to you.
🗂️ ** An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
🌐 OpenAI Compatible: Local server on port 1337 with OpenAI compatible endpoints
🌍 Open Source & Free: We build in public; check out our Github

About Jan

Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.

Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.

LlamaCorn-1.1B-Chat

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.9958	0.03	100	1.0003	-0.0002	-0.0002	0.4930	-0.0001	-180.9232	-195.6078	-2.6876	-2.6924
0.9299	1.02	3500	0.9439	-0.1570	-0.2195	0.5770	0.0625	-183.1160	-197.1755	-2.6612	-2.6663
0.9328	2.01	6900	0.9313	-0.2127	-0.2924	0.5884	0.0798	-183.8456	-197.7321	-2.6296	-2.6352
0.9321	2.98	10200	0.9305	-0.2149	-0.2955	0.5824	0.0805	-183.8759	-197.7545	-2.6439	-2.6493

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	36.94
AI2 Reasoning Challenge (25-Shot)	34.13
HellaSwag (10-Shot)	59.33
MMLU (5-Shot)	29.01
TruthfulQA (0-shot)	36.78
Winogrande (5-shot)	61.96
GSM8k (5-shot)	0.45

Downloads last month: 258

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/LlamaCorn-1.1B-Chat-GGUF

Base model

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

Quantized

(13)

this model

QuantFactory
/

LlamaCorn-1.1B-Chat-GGUF