Instructions to use clibrain/mamba-2.8b-chat-no_robots with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use clibrain/mamba-2.8b-chat-no_robots with Transformers:

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

pipe = pipeline("text-generation", model="clibrain/mamba-2.8b-chat-no_robots")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("clibrain/mamba-2.8b-chat-no_robots", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use clibrain/mamba-2.8b-chat-no_robots with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "clibrain/mamba-2.8b-chat-no_robots"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "clibrain/mamba-2.8b-chat-no_robots",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/clibrain/mamba-2.8b-chat-no_robots

SGLang

How to use clibrain/mamba-2.8b-chat-no_robots 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 "clibrain/mamba-2.8b-chat-no_robots" \
    --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": "clibrain/mamba-2.8b-chat-no_robots",
		"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 "clibrain/mamba-2.8b-chat-no_robots" \
        --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": "clibrain/mamba-2.8b-chat-no_robots",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use clibrain/mamba-2.8b-chat-no_robots with Docker Model Runner:
```
docker model run hf.co/clibrain/mamba-2.8b-chat-no_robots
```

MAMBA (2.8B) 🐍 fine-tuned on H4/no_robots dataset for chat / instruction

Model Card is still WIP!

Base model info

Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.

Dataset info

Look Ma, an instruction dataset that wasn't generated by GPTs!

Dataset Description

Repository: https://github.com/huggingface/alignment-handbook
Paper:
Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
Point of Contact: Lewis Tunstall

Dataset Summary

No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn instructions across the following categories:

Category	Count
Generation	4560
Open QA	1240
Brainstorm	1120
Chat	850
Rewrite	660
Summarize	420
Coding	350
Classify	350
Closed QA	260
Extract	190

Usage

pip install torch==2.1.0 transformers==4.35.0 causal-conv1d==1.0.0 mamba-ssm==1.0.1

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel

CHAT_TEMPLATE_ID = "HuggingFaceH4/zephyr-7b-beta"

device = "cuda:0" if torch.cuda.is_available() else "cpu"
model_name = "clibrain/mamba-2.8b-chat-no_robots"

eos_token = "<|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.eos_token = eos_token
tokenizer.pad_token = tokenizer.eos_token
tokenizer.chat_template = AutoTokenizer.from_pretrained(CHAT_TEMPLATE_ID).chat_template

model = MambaLMHeadModel.from_pretrained(
        model_name, device=device, dtype=torch.float16)

messages = []
prompt = "Tell me 5 sites to visit in Spain"
messages.append(dict(role="user", content=prompt))

input_ids = tokenizer.apply_chat_template(
            messages, return_tensors="pt", add_generation_prompt=True
).to(device)

out = model.generate(
    input_ids=input_ids,
    max_length=2000,
    temperature=0.9,
    top_p=0.7,
    eos_token_id=tokenizer.eos_token_id,
)

decoded = tokenizer.batch_decode(out)
assistant_message = (
    decoded[0].split("<|assistant|>\n")[-1].replace(eos_token, "")
)

print(assistant_message)

Gradio Demo

git clone https://github.com/mrm8488/mamba-chat.git
cd mamba-chat

pip install -r requirements.txt
pip install -q gradio==4.8.0

python app.py \
--model clibrain/mamba-2.8b-chat-no_robots \
--share

Evaluations

Coming soon!

Acknowledgments

Thanks to mamba-chat for heavily inspiring our work

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Dataset used to train clibrain/mamba-2.8b-chat-no_robots

Collection including clibrain/mamba-2.8b-chat-no_robots

🐍 Mamba fine-tuned models

Collection

A collection with ClibrAIn's Mamba fine-tuned models • 3 items • Updated Dec 18, 2023 • 11

Paper for clibrain/mamba-2.8b-chat-no_robots

Training language models to follow instructions with human feedback

Paper • 2203.02155 • Published Mar 4, 2022 • 24