Instructions to use tenyx/TenyxChat-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tenyx/TenyxChat-7B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="tenyx/TenyxChat-7B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tenyx/TenyxChat-7B-v1")
model = AutoModelForCausalLM.from_pretrained("tenyx/TenyxChat-7B-v1", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tenyx/TenyxChat-7B-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tenyx/TenyxChat-7B-v1

SGLang

How to use tenyx/TenyxChat-7B-v1 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 "tenyx/TenyxChat-7B-v1" \
    --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": "tenyx/TenyxChat-7B-v1",
		"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 "tenyx/TenyxChat-7B-v1" \
        --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": "tenyx/TenyxChat-7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tenyx/TenyxChat-7B-v1 with Docker Model Runner:
```
docker model run hf.co/tenyx/TenyxChat-7B-v1
```

shekkizh commited on Jan 6, 2024

Commit

75d20ad

1 Parent(s): 602f82f

adding model card

Browse files

Files changed (1) hide show

README.md +6 -2

README.md CHANGED Viewed

@@ -1,8 +1,6 @@
 ---
-model_type: Fine-tuned 7B model for chat.
 license: {apache-2.0}
 base_model: {openchat/openchat_3.5}
-demo: [Hugging Face Spaces](https://huggingface.co/spaces/tenyx/TenyxChat-7B-v1)
 ---
 # TenyxChat: Language Model Alignment using Tenyx Fine-tuning
@@ -11,6 +9,12 @@ Introducing TenyxChat, a series of ChatGPT-like models trained to function as us
 We fine-tune [Openchat-3.5](https://arxiv.org/pdf/2309.11235.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685), without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
 ## Usage

 ---
 license: {apache-2.0}
 base_model: {openchat/openchat_3.5}
 ---
 # TenyxChat: Language Model Alignment using Tenyx Fine-tuning
 We fine-tune [Openchat-3.5](https://arxiv.org/pdf/2309.11235.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685), without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
+ # Model details
+- Model type: Fine-tuned 7B model for chat.
+- License: Apache 2.0
+- Base model: OpenChat 3.5 ([https://huggingface.co/openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5))
+- Demo: Hugging face space
 ## Usage