Instructions to use clibrain/lince-zero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use clibrain/lince-zero with Transformers:

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

pipe = pipeline("text-generation", model="clibrain/lince-zero", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("clibrain/lince-zero", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("clibrain/lince-zero", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use clibrain/lince-zero with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "clibrain/lince-zero"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "clibrain/lince-zero",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/clibrain/lince-zero

SGLang

How to use clibrain/lince-zero 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/lince-zero" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "clibrain/lince-zero",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/lince-zero" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "clibrain/lince-zero",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use clibrain/lince-zero with Docker Model Runner:
```
docker model run hf.co/clibrain/lince-zero
```

mariagrandury commited on Jul 5, 2023

Commit

6fe7dc1

1 Parent(s): 76ca8d5

Update README.md

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@@ -75,7 +75,9 @@ LINCE-ZERO (Llm for Instructions from Natural Corpus en Español) is a state-of-
 LINCE-ZERO's fine-tuning on an instructions dataset enables it to follow natural language instructions in Spanish. The direct use cases include virtual assistants and content generation.
 Please note that running inference with LINCE-ZERO efficiently requires a minimum of XGB of memory.
 ## Downstream Use
@@ -115,7 +117,7 @@ After combining both translations, the dataset was augmented to reach a total of
 # ✅ Evaluation
-This is WIP.
 ### Results
@@ -240,7 +242,7 @@ There is a paper coming soon! Meanwhile, when using LINCE-ZERO please use the fo
 ```markdown
 @article{lince-zero,
   title={{LINCE-ZERO}: Llm for Instructions from Natural Corpus en Español},
-  author={},
   year={2023}
 }
 ```

 LINCE-ZERO's fine-tuning on an instructions dataset enables it to follow natural language instructions in Spanish. The direct use cases include virtual assistants and content generation.
+<!--
 Please note that running inference with LINCE-ZERO efficiently requires a minimum of XGB of memory.
+-->
 ## Downstream Use
 # ✅ Evaluation
+We are evaluating the model and will publish the results soon.
 ### Results
 ```markdown
 @article{lince-zero,
   title={{LINCE-ZERO}: Llm for Instructions from Natural Corpus en Español},
+  author={clibrain.com},
   year={2023}
 }
 ```