Instructions to use i3-lab/i3-4096ctx-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use i3-lab/i3-4096ctx-chat with Transformers:

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

pipe = pipeline("text-generation", model="i3-lab/i3-4096ctx-chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("i3-lab/i3-4096ctx-chat", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use i3-lab/i3-4096ctx-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "i3-lab/i3-4096ctx-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "i3-lab/i3-4096ctx-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/i3-lab/i3-4096ctx-chat

SGLang

How to use i3-lab/i3-4096ctx-chat 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 "i3-lab/i3-4096ctx-chat" \
    --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": "i3-lab/i3-4096ctx-chat",
		"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 "i3-lab/i3-4096ctx-chat" \
        --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": "i3-lab/i3-4096ctx-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use i3-lab/i3-4096ctx-chat with Docker Model Runner:
```
docker model run hf.co/i3-lab/i3-4096ctx-chat
```

i3 Hybrid Chat Model

This is a chat-tuned version of the i3 hybrid architecture with latent context compression.

Model Details

Architecture: RWKV + Attention Hybrid with Latent Compression
Parameters: ~342.4M
Context Window: 4096 tokens (via compression)
Inference Window: 4096 tokens
Kernel Size: 512 tokens
Training Data: HuggingFaceH4/ultrachat_200k

Usage

import torch
from tokenizers import Tokenizer

# Load model
model = torch.load("pytorch_model.bin")
tokenizer = Tokenizer.from_file("tokenizer.json")

# Format conversation
conversation = "<BOS><|user|>\nHello!\n<|assistant|>\n"
tokens = torch.tensor([tokenizer.encode(conversation).ids])

# Generate
output = model.generate(tokens, max_new_tokens=100, temperature=0.8)
response = tokenizer.decode(output[0].tolist())

Capabilities

Multi-turn conversations
Long context understanding via latent compression
Efficient inference with RWKV base layers
Ready for chain-of-thought fine-tuning

Training

Fine-tuned on UltraChat 200k dataset with:

Learning rate: 1e-05
Batch size: 4 × 4 accumulation
Sequence length: 512

Downloads last month: 404

Safetensors

Model size

0.3B params

Tensor type

F32

i3-lab
/

i3-4096ctx-chat

i3 Hybrid Chat Model

Model Details

Usage

Capabilities

Training

Space using i3-lab/i3-4096ctx-chat 1