Instructions to use ChocoLlama/ChocoLlama-2-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChocoLlama/ChocoLlama-2-7B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="ChocoLlama/ChocoLlama-2-7B-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChocoLlama/ChocoLlama-2-7B-instruct")
model = AutoModelForCausalLM.from_pretrained("ChocoLlama/ChocoLlama-2-7B-instruct", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ChocoLlama/ChocoLlama-2-7B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChocoLlama/ChocoLlama-2-7B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChocoLlama/ChocoLlama-2-7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ChocoLlama/ChocoLlama-2-7B-instruct

SGLang

How to use ChocoLlama/ChocoLlama-2-7B-instruct 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 "ChocoLlama/ChocoLlama-2-7B-instruct" \
    --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": "ChocoLlama/ChocoLlama-2-7B-instruct",
		"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 "ChocoLlama/ChocoLlama-2-7B-instruct" \
        --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": "ChocoLlama/ChocoLlama-2-7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ChocoLlama/ChocoLlama-2-7B-instruct with Docker Model Runner:
```
docker model run hf.co/ChocoLlama/ChocoLlama-2-7B-instruct
```

matthieumeeus97 commited on Dec 11, 2024

Commit

a859a39

verified ·

1 Parent(s): 60295dc

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -77,7 +77,7 @@ We provide 6 variants (of which 3 base and 3 instruction-tuned models):
 - **Llama-3-ChocoLlama-8B-base** ([link](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-base)): A language-adapted version of Meta's Llama-8-8B, fine-tuned on the same Dutch dataset as ChocoLlama-2-7B-base, again using LoRa.
 - **Llama-3-ChocoLlama-instruct** ([link](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-instruct)): An instruction-tuned version of Llama-3-ChocoLlama-8B-base, fine-tuned on the same dataset as ChocoLlama-2-7B-instruct, again using SFT followed by DPO.
-For benchmark results for all models, including compared to their base models and other Dutch LLMs, we refer to our paper [here](some_url).
 ### Model Description
@@ -89,8 +89,8 @@ For benchmark results for all models, including compared to their base models an
 ### Model Sources
-- **Repository:** Will be released soon.
-- **Paper:** Will be released soon.
 ## Uses

 - **Llama-3-ChocoLlama-8B-base** ([link](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-base)): A language-adapted version of Meta's Llama-8-8B, fine-tuned on the same Dutch dataset as ChocoLlama-2-7B-base, again using LoRa.
 - **Llama-3-ChocoLlama-instruct** ([link](https://huggingface.co/ChocoLlama/Llama-3-ChocoLlama-8B-instruct)): An instruction-tuned version of Llama-3-ChocoLlama-8B-base, fine-tuned on the same dataset as ChocoLlama-2-7B-instruct, again using SFT followed by DPO.
+For benchmark results for all models, including compared to their base models and other Dutch LLMs, we refer to our paper [here](https://arxiv.org/pdf/2412.07633).
 ### Model Description
 ### Model Sources
+- **Repository:** [on Github here](https://github.com/ChocoLlamaModel/ChocoLlama).
+- **Paper:** [on ArXiv here](https://arxiv.org/pdf/2412.07633).
 ## Uses