Instructions to use alivi/fine-tuning_Zephyr-7b_SpanishQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alivi/fine-tuning_Zephyr-7b_SpanishQA with Transformers:

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

pipe = pipeline("text-generation", model="alivi/fine-tuning_Zephyr-7b_SpanishQA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alivi/fine-tuning_Zephyr-7b_SpanishQA")
model = AutoModelForCausalLM.from_pretrained("alivi/fine-tuning_Zephyr-7b_SpanishQA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alivi/fine-tuning_Zephyr-7b_SpanishQA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alivi/fine-tuning_Zephyr-7b_SpanishQA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alivi/fine-tuning_Zephyr-7b_SpanishQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alivi/fine-tuning_Zephyr-7b_SpanishQA

SGLang

How to use alivi/fine-tuning_Zephyr-7b_SpanishQA 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 "alivi/fine-tuning_Zephyr-7b_SpanishQA" \
    --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": "alivi/fine-tuning_Zephyr-7b_SpanishQA",
		"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 "alivi/fine-tuning_Zephyr-7b_SpanishQA" \
        --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": "alivi/fine-tuning_Zephyr-7b_SpanishQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alivi/fine-tuning_Zephyr-7b_SpanishQA with Docker Model Runner:
```
docker model run hf.co/alivi/fine-tuning_Zephyr-7b_SpanishQA
```

alivi commited on Jun 18, 2024

Commit

0e2e650

verified ·

1 Parent(s): 9f3e643

Upload model

Browse files

Files changed (3) hide show

README.md +3 -3
adapter_config.json +2 -2
adapter_model.safetensors +1 -1

README.md CHANGED Viewed

@@ -1,10 +1,10 @@
 ---
-library_name: transformers
-datasets:
-- alivi/QASpanish
 language:
 - en
 - es
 pipeline_tag: text-generation
 ---

 ---
 language:
 - en
 - es
+library_name: transformers
+datasets:
+- alivi/QASpanish
 pipeline_tag: text-generation
 ---

adapter_config.json CHANGED Viewed

@@ -20,8 +20,8 @@
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   "revision": null,
   "target_modules": [
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   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

   "rank_pattern": {},
   "revision": null,
   "target_modules": [
+    "v_proj",
+    "q_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

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