Instructions to use kevin009/lamatama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kevin009/lamatama with Transformers:

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

pipe = pipeline("text-generation", model="kevin009/lamatama")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kevin009/lamatama")
model = AutoModelForCausalLM.from_pretrained("kevin009/lamatama")
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

vLLM

How to use kevin009/lamatama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kevin009/lamatama"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kevin009/lamatama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kevin009/lamatama

SGLang

How to use kevin009/lamatama 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 "kevin009/lamatama" \
    --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": "kevin009/lamatama",
		"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 "kevin009/lamatama" \
        --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": "kevin009/lamatama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kevin009/lamatama with Docker Model Runner:
```
docker model run hf.co/kevin009/lamatama
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card: kevin009/lamatama

Model Description

The kevin009/lamatama model is a groundbreaking achievement in the field of language modeling, showcasing the power of leveraging a substantial dataset and state-of-the-art training techniques. This model is designed to push the boundaries of what's possible in natural language understanding and generation.

Training Details

Model Architecture: The kevin009/lamatama model is built upon the architecture and tokenizer of Llama 2, ensuring compatibility and easy integration with various open-source projects.
Dataset: It was pretrained on an impressive 3 trillion tokens, a scale that allows for a deep and nuanced understanding of language.
Training Period: The training process was carried out over 90 days, utilizing 16 A100-40G GPUs, a testament to the model's efficiency and the team's optimization skills.

Fine-tuning

This specific version of the model has been fine-tuned to excel in chat-based applications. It builds upon the TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T model, incorporating learnings and optimizations from HF's Zephyr's training recipe.

Initial Phase: The model was first fine-tuned on a variant of the UltraChat dataset, which is rich in synthetic dialogues generated by ChatGPT.
Further Alignment: Subsequent alignment was achieved using 🤗 TRL's DPOTrainer with the openbmb/UltraFeedback dataset, comprising 64k prompts and model completions ranked by GPT-4.

How to Use

Ensure you have transformers>=4.34. For detailed instructions and updates, check out the GitHub page for kevin009/lamatama.

Installation (for versions <= v4.34)

pip install git+https://github.com/huggingface/transformers.git
pip install accelerate

Example Usage

Here's a quick guide on using kevin009/lamatama for generating text:

import torch
from transformers import pipeline

# Initialize the pipeline
pipe = pipeline("text-generation", model="kevin009/lamatama", torch_dtype=torch.bfloat16, device_map="auto")

# Sample dialogue with templating
messages = [
    {"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}
]

# Generate prompt and outputs
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Acknowledgements

This model is a product of collaboration and innovative approaches to language modeling. We extend our thanks to all contributors, as well as the creators of the datasets and training methodologies that made kevin009/lamatama a reality.

This model card introduces kevin009/lamatama, a versatile and powerful language model fine-tuned for chat applications, demonstrating exceptional understanding and generation capabilities.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	37.15
AI2 Reasoning Challenge (25-Shot)	36.35
HellaSwag (10-Shot)	61.12
MMLU (5-Shot)	24.72
TruthfulQA (0-shot)	37.67
Winogrande (5-shot)	60.77
GSM8k (5-shot)	2.27

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for kevin009/lamatama

Merges

1 model

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

36.350
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

61.120
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

24.720
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

37.670
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

60.770
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

2.270