Instructions to use fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned with Transformers:

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

pipe = pipeline("text-generation", model="fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned")

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

tokenizer = AutoTokenizer.from_pretrained("fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned")
model = AutoModelForCausalLM.from_pretrained("fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned

SGLang

How to use fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned 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 "fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned" \
    --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": "fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned",
		"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 "fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned" \
        --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": "fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned with Docker Model Runner:
```
docker model run hf.co/fahmizainal17/Meta-Llama-3-8B-Instruct-fine-tuned
```

fahmizainal17 commited on Nov 13, 2024

Commit

93b728d

verified ·

1 Parent(s): c88f83c

Update README.md

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README.md +39 -6

README.md CHANGED Viewed

@@ -1,6 +1,37 @@
 ```yaml
 library_name: transformers
-tags: [language-model, causal-language-model, instruction-tuned, advanced, quantized]
 ---
 # Model Card for fahmizainal17/meta-llama-3b-instruct-advanced
@@ -16,14 +47,14 @@ This model is a variant of **Meta LLaMA 3B**, fine-tuned with instruction-follow
 - **Developed by:** fahmizainal17
 - **Model type:** Causal Language Model
 - **Language(s) (NLP):** English (potentially adaptable to other languages with additional fine-tuning)
-- **License:** Open-Source, MIT License
 - **Finetuned from model:** Meta-LLaMA-3B
 ### Model Sources
-- **Repository:** [Hugging Face model page link](https://huggingface.co/fahmizainal17/meta-llama-3b-instruct-advanced)
 - **Paper:** [Meta-LLaMA Paper](https://arxiv.org/abs/2301.10345) (Meta LLaMA Base Paper)
-- **Demo:** [Model demo hosted link] (or placeholder if unavailable)
 ## Uses
@@ -186,7 +217,9 @@ Fahmizainal17. (2024). *Meta-LLaMA 3B Instruct Advanced*. Hugging Face. Retrieve
 ## More Information
-For further details on the model's performance, use cases, or licensing, please contact the author or visit the Hugging Face model page.
 ## Model Card Authors
@@ -198,4 +231,4 @@ For further inquiries, please contact fahmizainal@invokeisdata.com.
 ```
----

 ```yaml
 library_name: transformers
+tags:
+  - language-model
+  - causal-language-model
+  - instruction-tuned
+  - advanced
+  - quantized
+datasets:
+  - custom-instruction-following-dataset
+metrics:
+  - accuracy
+  - response-quality
+license: mit
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+pipeline_tag: text-generation
+language:
+  - en
+thumbnail: "url-to-thumbnail"
+new_version: fahmizainal17/meta-llama-3b-instruct-advanced-v2
+model-index:
+  - name: meta-llama-3b-instruct-advanced
+    results:
+      - task:
+          type: text-generation
+        dataset:
+          name: custom-instruction-following-dataset
+          type: instruction-following
+        metrics:
+          - name: Accuracy
+            value: 89.5
+        source:
+          name: Open LLM Leaderboard
+          url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
 ---
 # Model Card for fahmizainal17/meta-llama-3b-instruct-advanced
 - **Developed by:** fahmizainal17
 - **Model type:** Causal Language Model
 - **Language(s) (NLP):** English (potentially adaptable to other languages with additional fine-tuning)
+- **License:** MIT
 - **Finetuned from model:** Meta-LLaMA-3B
 ### Model Sources
+- **Repository:** [Hugging Face model page](https://huggingface.co/fahmizainal17/meta-llama-3b-instruct-advanced)
 - **Paper:** [Meta-LLaMA Paper](https://arxiv.org/abs/2301.10345) (Meta LLaMA Base Paper)
+- **Demo:** [Model demo link] (or placeholder if available)
 ## Uses
 ## More Information
+For further details
+ on the model's performance, use cases, or licensing, please contact the author or visit the Hugging Face model page.
 ## Model Card Authors
 ```
+---