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

Libraries

How to use AbdulrhmanEldeeb/lora_model with Transformers:

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

pipe = pipeline("text-generation", model="AbdulrhmanEldeeb/lora_model")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AbdulrhmanEldeeb/lora_model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AbdulrhmanEldeeb/lora_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AbdulrhmanEldeeb/lora_model

SGLang

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

Unsloth Desktop
Docker Model Runner
How to use AbdulrhmanEldeeb/lora_model with Docker Model Runner:
```
docker model run hf.co/AbdulrhmanEldeeb/lora_model
```

Eldeeb commited on Dec 1, 2024

Commit

dc5b99b

verified ·

1 Parent(s): 41db25d

Update README.md

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README.md +23 -30

README.md CHANGED Viewed

@@ -1,19 +1,19 @@
----
-base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
-license: apache-2.0
-language:
-- en
-- ar
-datasets:
-- Abdulrhman37/metallurgy-qa
-pipeline_tag: text2text-generation
----
 # Fine-Tuned Llama Model for Metallurgy and Materials Science
@@ -51,22 +51,15 @@ Fine-tuning focused on enhancing domain-specific knowledge using a dataset curat
 ## 📦 How to Use
-You can load the model using the `transformers` library:
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("Abdulrhman37/metallurgy-llama")
-model = AutoModelForCausalLM.from_pretrained("Abdulrhman37/metallurgy-llama")
-# Example Query
-prompt = "Explain the role of manganese in Mg-Al-Mn systems."
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_new_tokens=150)
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(response)
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

+---
+base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- en
+- ar
+datasets:
+- Abdulrhman37/metallurgy-qa
+pipeline_tag: text2text-generation
+---
 # Fine-Tuned Llama Model for Metallurgy and Materials Science
 ## 📦 How to Use
+follow this [notebook](https://colab.research.google.com/drive/1pRNcAtybNF6w6mE1ZReFwfrIujZ5_t4S#scrollTo=wk4fCWOl0Ocd) for help to use the model
+## 📧 Contact
+For any inquiries, feedback, or collaboration opportunities, feel free to reach out:
+- Email: [abdodebo3@gmail.com](mailto:abdodebo3@gmail.com)
+- [LinkedIn](https://www.linkedin.com/in/abdulrahman-eldeeb-8b4621253/)
+- [GitHub](https://github.com/AdbulrhmanEldeeb)
+- Phone: +20 1026821545
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.