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

Libraries

How to use beowolx/MistralHermes-CodePro-7B-v1 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beowolx/MistralHermes-CodePro-7B-v1")
model = AutoModelForCausalLM.from_pretrained("beowolx/MistralHermes-CodePro-7B-v1")
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

vLLM

How to use beowolx/MistralHermes-CodePro-7B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beowolx/MistralHermes-CodePro-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": "beowolx/MistralHermes-CodePro-7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beowolx/MistralHermes-CodePro-7B-v1

SGLang

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

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

Adding Evaluation Results

by leaderboard-pr-bot - opened Mar 4, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+18

-5

Files changed (1) hide show

README.md +18 -5

README.md CHANGED Viewed

@@ -1,5 +1,7 @@
 ---
-base_model: teknium/OpenHermes-2.5-Mistral-7B
 tags:
 - mistral
 - instruct
@@ -8,12 +10,10 @@ tags:
 - gpt4
 - synthetic data
 - distillation
 model-index:
 - name: MistralHermes-CodePro-7B-v1
   results: []
-license: mit
-language:
-- en
 ---
 # MistralHermes-CodePro-7B-v1
@@ -38,4 +38,17 @@ You should use [LM Studio](https://lmstudio.ai/) for chatting with the model.
 # Quantized Models:
-GGUF: [beowolx/MistralHermes-CodePro-7B-v1-GGUF](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1-GGUF)

 ---
+language:
+- en
+license: mit
 tags:
 - mistral
 - instruct
 - gpt4
 - synthetic data
 - distillation
+base_model: teknium/OpenHermes-2.5-Mistral-7B
 model-index:
 - name: MistralHermes-CodePro-7B-v1
   results: []
 ---
 # MistralHermes-CodePro-7B-v1
 # Quantized Models:
+GGUF: [beowolx/MistralHermes-CodePro-7B-v1-GGUF](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1-GGUF)
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_beowolx__MistralHermes-CodePro-7B-v1)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |66.17|
+|AI2 Reasoning Challenge (25-Shot)|62.46|
+|HellaSwag (10-Shot)              |82.68|
+|MMLU (5-Shot)                    |63.44|
+|TruthfulQA (0-shot)              |49.67|
+|Winogrande (5-shot)              |77.90|
+|GSM8k (5-shot)                   |60.88|