Instructions to use abhinand/malayalam-llama-7b-instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinand/malayalam-llama-7b-instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="abhinand/malayalam-llama-7b-instruct-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("abhinand/malayalam-llama-7b-instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("abhinand/malayalam-llama-7b-instruct-v0.1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abhinand/malayalam-llama-7b-instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhinand/malayalam-llama-7b-instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinand/malayalam-llama-7b-instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abhinand/malayalam-llama-7b-instruct-v0.1

SGLang

How to use abhinand/malayalam-llama-7b-instruct-v0.1 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 "abhinand/malayalam-llama-7b-instruct-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinand/malayalam-llama-7b-instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "abhinand/malayalam-llama-7b-instruct-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhinand/malayalam-llama-7b-instruct-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abhinand/malayalam-llama-7b-instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/abhinand/malayalam-llama-7b-instruct-v0.1
```

leaderboard-pr-bot commited on Mar 4, 2024

Commit

dc0d425

verified ·

1 Parent(s): 77591fc

Adding Evaluation Results

Browse files

This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

Files changed (1) hide show

README.md +18 -5

README.md CHANGED Viewed

@@ -1,12 +1,12 @@
 ---
 base_model: abhinand/malayalam-llama-pt-v0
 model-index:
 - name: malayalam-llama-instruct-v0.1
   results: []
-license: llama2
-language:
-- en
-- ml
 ---
 # Malayalam LLaMA 7B Instruct v0.1
@@ -173,4 +173,17 @@ If you use this model or any of the the Tamil-Llama related work in your researc
 }
 ```
-We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.

 ---
+language:
+- en
+- ml
+license: llama2
 base_model: abhinand/malayalam-llama-pt-v0
 model-index:
 - name: malayalam-llama-instruct-v0.1
   results: []
 ---
 # Malayalam LLaMA 7B Instruct v0.1
 }
 ```
+We hope this model serves as a valuable tool in your NLP toolkit and look forward to seeing the advancements it will enable in the understanding and generation of the Tamil language.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_abhinand__malayalam-llama-7b-instruct-v0.1)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |39.69|
+|AI2 Reasoning Challenge (25-Shot)|37.20|
+|HellaSwag (10-Shot)              |67.81|
+|MMLU (5-Shot)                    |23.12|
+|TruthfulQA (0-shot)              |47.11|
+|Winogrande (5-shot)              |62.90|
+|GSM8k (5-shot)                   | 0.00|