Instructions to use recogna-nlp/internlm-chatbode-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use recogna-nlp/internlm-chatbode-20b with Transformers:

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

pipe = pipeline("text-generation", model="recogna-nlp/internlm-chatbode-20b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("recogna-nlp/internlm-chatbode-20b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use recogna-nlp/internlm-chatbode-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "recogna-nlp/internlm-chatbode-20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "recogna-nlp/internlm-chatbode-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/recogna-nlp/internlm-chatbode-20b

SGLang

How to use recogna-nlp/internlm-chatbode-20b 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 "recogna-nlp/internlm-chatbode-20b" \
    --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": "recogna-nlp/internlm-chatbode-20b",
		"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 "recogna-nlp/internlm-chatbode-20b" \
        --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": "recogna-nlp/internlm-chatbode-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use recogna-nlp/internlm-chatbode-20b with Docker Model Runner:
```
docker model run hf.co/recogna-nlp/internlm-chatbode-20b
```

internlm-chatbode-20b

ChatBode Logo

O InternLm-ChatBode é um modelo de linguagem ajustado para o idioma português, desenvolvido a partir do modelo InternLM2. Este modelo foi refinado através do processo de fine-tuning utilizando o dataset UltraAlpaca.

Características Principais

Modelo Base: internlm/internlm2-chat-20b
Dataset para Fine-tuning: UltraAlpaca
Treinamento: O treinamento foi realizado a partir do fine-tuning, usando QLoRA, do internlm2-chat-20b.

Exemplo de uso

A seguir um exemplo de código de como carregar e utilizar o modelo:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("recogna-nlp/internlm-chatbode-20b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("recogna-nlp/internlm-chatbode-20b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "Olá", history=[])
print(response)
response, history = model.chat(tokenizer, "O que é o Teorema de Pitágoras? Me dê um exemplo", history=history)
print(response)

As respostas podem ser geradas via stream utilizando o método stream_chat:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "recogna-nlp/internlm-chatbode-20b"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
length = 0
for response, history in model.stream_chat(tokenizer, "Olá", history=[]):
    print(response[length:], flush=True, end="")
    length = len(response)

Open Portuguese LLM Leaderboard Evaluation Results

Detailed results can be found here and on the 🚀 Open Portuguese LLM Leaderboard

Metric	Value
Average	71.68
ENEM Challenge (No Images)	65.78
BLUEX (No Images)	58.69
OAB Exams	43.33
Assin2 RTE	91.53
Assin2 STS	78.95
FaQuAD NLI	81.36
HateBR Binary	81.72
PT Hate Speech Binary	73.66
tweetSentBR	70.11

Downloads last month: 20

Safetensors

Model size

20B params

Tensor type

F16

Space using recogna-nlp/internlm-chatbode-20b 1

Evaluation results

accuracy on ENEM Challenge (No Images)
Open Portuguese LLM Leaderboard

65.780
accuracy on BLUEX (No Images)
Open Portuguese LLM Leaderboard

58.690
accuracy on OAB Exams
Open Portuguese LLM Leaderboard

43.330
f1-macro on Assin2 RTE
test set Open Portuguese LLM Leaderboard

91.530
pearson on Assin2 STS
test set Open Portuguese LLM Leaderboard

78.950
f1-macro on FaQuAD NLI
test set Open Portuguese LLM Leaderboard

81.360
f1-macro on HateBR Binary
test set Open Portuguese LLM Leaderboard

81.720
f1-macro on PT Hate Speech Binary
test set Open Portuguese LLM Leaderboard

73.660