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

Libraries

How to use jan-hq/LlamaCorn-1.1B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jan-hq/LlamaCorn-1.1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jan-hq/LlamaCorn-1.1B")
model = AutoModelForMultimodalLM.from_pretrained("jan-hq/LlamaCorn-1.1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jan-hq/LlamaCorn-1.1B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jan-hq/LlamaCorn-1.1B

SGLang

How to use jan-hq/LlamaCorn-1.1B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jan-hq/LlamaCorn-1.1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jan-hq/LlamaCorn-1.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jan-hq/LlamaCorn-1.1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jan-hq/LlamaCorn-1.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jan-hq/LlamaCorn-1.1B with Docker Model Runner:
```
docker model run hf.co/jan-hq/LlamaCorn-1.1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Jan - Discord

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-sft-adapter

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the jan-hq/bagel_sft_binarized, the jan-hq/dolphin_binarized and the jan-hq/openhermes_binarized datasets. It achieves the following results on the evaluation set:

Loss: 0.9638

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: 7e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
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
1.038	1.0	6606	1.0506
0.876	2.0	13212	0.9648
0.7713	3.0	19818	0.9638

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+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: 5

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for jan-hq/LlamaCorn-1.1B

Base model

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

Finetuned

(105)

this model

Quantizations

2 models

jan-hq
/

LlamaCorn-1.1B

Prompt template

Run this model

About Jan

LlamaCorn-sft-adapter

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Open LLM Leaderboard Evaluation Results

Model tree for jan-hq/LlamaCorn-1.1B

Datasets used to train jan-hq/LlamaCorn-1.1B