Instructions to use CausalLM/35b-beta2ep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CausalLM/35b-beta2ep with Transformers:

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

pipe = pipeline("text-generation", model="CausalLM/35b-beta2ep")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CausalLM/35b-beta2ep")
model = AutoModelForCausalLM.from_pretrained("CausalLM/35b-beta2ep")
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 CausalLM/35b-beta2ep with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CausalLM/35b-beta2ep"
# 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/35b-beta2ep",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CausalLM/35b-beta2ep

SGLang

How to use CausalLM/35b-beta2ep 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/35b-beta2ep" \
    --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/35b-beta2ep",
		"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/35b-beta2ep" \
        --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/35b-beta2ep",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CausalLM/35b-beta2ep with Docker Model Runner:
```
docker model run hf.co/CausalLM/35b-beta2ep
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Tokenizer is different from cohere - and chat template is ChatML - fully fine-tuned at 128K+ ~ 30M entries long, web crawl input, GPT-4-32k/3.5-16k output, synthetic dataset - 1 epoch

For another candidate version of 1 epoch - https://huggingface.co/CausalLM/35b-beta - somehow less overfitting?

No loras, no quants, no tricks.

This one is not "very 128k", use https://huggingface.co/CausalLM/35b-beta-long for long context. But better in general tasks, knowledge, coding and so on.

And, merge them if you want!

Downloads last month: 19

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for CausalLM/35b-beta2ep

Quantizations

1 model

Datasets used to train CausalLM/35b-beta2ep

Collection including CausalLM/35b-beta2ep

34B & 35B

Collection

5 items • Updated Aug 25, 2024 • 3