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
```

Sarath Shekkizhar commited on Jan 10, 2024

Commit

1edad18

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@@ -128,7 +128,7 @@ These benchmarks test reasoning and knowledge in various tasks in few-shot setti
 | Mistral-7B | 62.4 | 74.0 | 38.1 | 57.2 | 62.8 | 37.8 | 55.38 |
 | OpenLLM Leader-7B  | 64.3 | 78.7 | 73.3 | 66.6 | 68.4 | 58.5 | 68.3 |
-**Note:** While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation [above](https://www.notion.so/TenyxChat-Language-Model-Alignment-using-Tenyx-Fine-tuning-30e60a53d17a46b0a4755c74f0f8b222?pvs=21)). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as *catastrophic forgetting*. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.
 # Limitations

 | Mistral-7B | 62.4 | 74.0 | 38.1 | 57.2 | 62.8 | 37.8 | 55.38 |
 | OpenLLM Leader-7B  | 64.3 | 78.7 | 73.3 | 66.6 | 68.4 | 58.5 | 68.3 |
+**Note:** While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation [above](#comparison-with-additional-open-llm-leaderboard-models)). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as *catastrophic forgetting*. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.
 # Limitations