Instructions to use CausalLM/34b-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CausalLM/34b-beta with Transformers:

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

pipe = pipeline("text-generation", model="CausalLM/34b-beta")
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("CausalLM/34b-beta")
model = AutoModelForCausalLM.from_pretrained("CausalLM/34b-beta", 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 CausalLM/34b-beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CausalLM/34b-beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CausalLM/34b-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CausalLM/34b-beta

SGLang

How to use CausalLM/34b-beta 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 "CausalLM/34b-beta" \
    --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": "CausalLM/34b-beta",
		"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 "CausalLM/34b-beta" \
        --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": "CausalLM/34b-beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CausalLM/34b-beta with Docker Model Runner:
```
docker model run hf.co/CausalLM/34b-beta
```

JosephusCheung commited on Feb 9, 2024

Commit

aa2b1b7

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@@ -1,8 +1,12 @@
 ---
-license: wtfpl
 ---
 # CausalLM 34B β
 ## MT-Bench: 8.5
 ![mt-bench](https://cdn-uploads.huggingface.co/production/uploads/63468a143ea42ee2cb49ddd1/2vv2_nGbfWuOM8jwy40dn.png)

 ---
+license: gpl
 ---
 # CausalLM 34B β
+There are some issues with the model weights in terms of precision. In the next version update, we will roll back some progress and retrain to fix these issues as soon as possible.
+Please note: Do not use "accelerated inference frameworks" like VLLM temporarily. Instead, use Transformers for inference. Otherwise, due to precision issues, the output quality will be significantly degraded. If you need faster inference, you can consider using the q8_0 quantization (faster and better than bf16 vllm for this model only) with llama.cpp temporarily or wait for the official version.
+Some minor details will be fixed in the upcoming next version update.
 ## MT-Bench: 8.5
 ![mt-bench](https://cdn-uploads.huggingface.co/production/uploads/63468a143ea42ee2cb49ddd1/2vv2_nGbfWuOM8jwy40dn.png)