Instructions to use pankajmathur/model_101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajmathur/model_101 with Transformers:

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

pipe = pipeline("text-generation", model="pankajmathur/model_101")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pankajmathur/model_101")
model = AutoModelForCausalLM.from_pretrained("pankajmathur/model_101", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pankajmathur/model_101 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajmathur/model_101"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajmathur/model_101",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pankajmathur/model_101

SGLang

How to use pankajmathur/model_101 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 "pankajmathur/model_101" \
    --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": "pankajmathur/model_101",
		"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 "pankajmathur/model_101" \
        --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": "pankajmathur/model_101",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pankajmathur/model_101 with Docker Model Runner:
```
docker model run hf.co/pankajmathur/model_101
```

pankajmathur commited on Nov 9, 2023

Commit

a64eeb1

1 Parent(s): 7fde4e7

Update README.md

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README.md +11 -8

README.md CHANGED Viewed

@@ -53,14 +53,17 @@ We evaluated model_001 on a wide range of tasks using [Language Model Evaluation
 Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-|||||
-|:------:|:--------:|:-------:|:--------:|
-|**Task**|**Metric**|**Value**|**Stderr**|
-|*arc_challenge*|acc_norm|0.7108|0.0141|
-|*hellaswag*|acc_norm|0.8765|0.0038|
-|*mmlu*|acc_norm|0.6904|0.0351|
-|*truthfulqa_mc*|mc2|0.6312|0.0157|
-|**Total Average**|-|**0.72729**||
 <br>

 Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+|||
+|:------:|:-------:|
+|**Task**|**Value**|
+|*ARC*|0.6869|
+|*HellaSwag*|0.8642|
+|*MMLU*|0.6992|
+|*TruthfulQA*|0.5885|
+|*Winogrande*|0.8208|
+|*GSM8k*|0.4481|
+|*DROP*|0.5510|
+|**Total Average**|**0.6655**|
 <br>