Instructions to use solvrays/solvrays-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solvrays/solvrays-llm with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solvrays/solvrays-llm")
model = AutoModelForCausalLM.from_pretrained("solvrays/solvrays-llm")
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 solvrays/solvrays-llm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/solvrays/solvrays-llm

SGLang

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

Docker Model Runner
How to use solvrays/solvrays-llm with Docker Model Runner:
```
docker model run hf.co/solvrays/solvrays-llm
```

singtan commited on 8 days ago

Commit

fbbccdc

verified ·

1 Parent(s): 8bdabf8

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -34,16 +34,16 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto', torch_dtype=torch.float16)
 # Universal Grounding Template
-instruction = 'Analyze the document and provide a precise response based strictly on the content provided.'
-source_doc = 'Architecture_Spec.pdf'
 query = 'What are the main infrastructure requirements?'
 prompt = (f'### Instruction: {instruction}
 '
-          f'### Source: {source_doc}
-'
-          f'### Content: {query}
-'
           f'### Verified Response:')
 inputs = tokenizer(prompt, return_tensors='pt').to(model.device)

 model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto', torch_dtype=torch.float16)
 # Universal Grounding Template
+instruction = 'Analyze the following document and provide a precise, factual response based strictly on the content provided. If the information is not present, you must state that it is not documented.'
 query = 'What are the main infrastructure requirements?'
+prompt = (f'### Instruction: {instruction}\n'
+          f'### Knowledge Context: {query}\n'
+          f'### Verified Response:')
 prompt = (f'### Instruction: {instruction}
 '
+### Knowledge Context: Extract the overview and key details from this document.
           f'### Verified Response:')
 inputs = tokenizer(prompt, return_tensors='pt').to(model.device)