Text Generation
Transformers
Safetensors
Portuguese
internlm2
feature-extraction
conversational
custom_code
Eval Results (legacy)
Instructions to use recogna-nlp/internlm-chatbode-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use recogna-nlp/internlm-chatbode-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="recogna-nlp/internlm-chatbode-7b", 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-7b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use recogna-nlp/internlm-chatbode-7b 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-7b" # 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-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/recogna-nlp/internlm-chatbode-7b
- SGLang
How to use recogna-nlp/internlm-chatbode-7b 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-7b" \ --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-7b", "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-7b" \ --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-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use recogna-nlp/internlm-chatbode-7b with Docker Model Runner:
docker model run hf.co/recogna-nlp/internlm-chatbode-7b
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library_name: transformers
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model-index:
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name: Open Portuguese LLM Leaderboard
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# internlm-chatbode-7b
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|FaQuAD NLI | 79.80|
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|HateBR Binary | 87.99|
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|PT Hate Speech Binary | 68.09|
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|tweetSentBR | 61.11|
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library_name: transformers
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results:
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value: 63.05
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name: accuracy
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url: >-
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https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/internlm-chatbode-7b
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name: Open Portuguese LLM Leaderboard
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type: text-generation
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value: 51.46
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name: accuracy
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https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/internlm-chatbode-7b
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name: Open Portuguese LLM Leaderboard
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value: 42.32
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name: accuracy
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value: 91.33
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name: Open Portuguese LLM Leaderboard
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value: 80.69
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name: pearson
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name: Open Portuguese LLM Leaderboard
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value: 68.09
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name: Open Portuguese LLM Leaderboard
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type: text-generation
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value: 61.11
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name: f1-macro
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name: Open Portuguese LLM Leaderboard
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language:
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- pt
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pipeline_tag: text-generation
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---
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# internlm-chatbode-7b
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|FaQuAD NLI | 79.80|
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|HateBR Binary | 87.99|
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|PT Hate Speech Binary | 68.09|
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|tweetSentBR | 61.11|
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