Instructions to use gbstox/agronomYi-hermes-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gbstox/agronomYi-hermes-34B with Transformers:

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

pipe = pipeline("text-generation", model="gbstox/agronomYi-hermes-34B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gbstox/agronomYi-hermes-34B")
model = AutoModelForCausalLM.from_pretrained("gbstox/agronomYi-hermes-34B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gbstox/agronomYi-hermes-34B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gbstox/agronomYi-hermes-34B

SGLang

How to use gbstox/agronomYi-hermes-34B 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 "gbstox/agronomYi-hermes-34B" \
    --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": "gbstox/agronomYi-hermes-34B",
		"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 "gbstox/agronomYi-hermes-34B" \
        --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": "gbstox/agronomYi-hermes-34B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gbstox/agronomYi-hermes-34B with Docker Model Runner:
```
docker model run hf.co/gbstox/agronomYi-hermes-34B
```

AgronomYi-hermes-34B

About

AgronomYi is a fine tune of Nous-Hermes-2-Yi-34B, which uses Yi-34B as the base model. I fine tuned this with agronomy data (exclusively textbooks & university extension guides), full training data set here). AgronomYi outperforms all models on the benchmark except for gpt-4, and consistently beats the base model by 7-9% and the hermes fine tune by 3-5%. I take this to mean that even better results can be acheived with additional fine tuning, and larger models tend to perform better in general.

Benchmark comparison

benchmark info here

Model Name	Score	Date Tested
gpt-4	85.71%	2024-01-15
agronomYi-hermes-34b	79.05%	2024-01-15
mistral-medium	77.14%	2024-01-15
nous-hermes-yi-34b	76.19%	2024-01-15
mixtral-8x7b-instruct	72.38%	2024-01-15
claude-2	72.38%	2024-01-15
yi-34b-chat	71.43%	2024-01-15
norm	69.52%	2024-01-17
openhermes-2.5-mistral-7b	69.52%	2024-01-15
gpt-3.5-turbo	67.62%	2024-01-15
mistral-7b-instruct	61.9%	2024-01-15

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Model tree for gbstox/agronomYi-hermes-34B

Base model

01-ai/Yi-34B

Finetuned

NousResearch/Nous-Hermes-2-Yi-34B

Finetuned

(4)

this model

gbstox
/

agronomYi-hermes-34B

AgronomYi-hermes-34B

About

Benchmark comparison

Model tree for gbstox/agronomYi-hermes-34B

Dataset used to train gbstox/agronomYi-hermes-34B