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", 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 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
```

solvrays-llm / README.md

singtan

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8aacd19 verified 3 months ago

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	---
	base_model: google/gemma-2b-it
	language: en
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- precision-grounding
	- document-qa
	- zero-hallucination
	- legal-tech
	- technical-analysis
	---

	# 📂 Solvrays Llm - High Precision Document Analyst
	\n## 🌟 Overview
	This model is a specialized fine-tuning of google/gemma-2b-it, engineered for Zero-Hallucination Document Retrieval. It has been optimized to handle complex, domain-specific documents (Technical, Legal, or Architectural) with strict adherence to provided context.
	\n### 🛠 Primary Design Objectives
	- Factual Integrity: Programmed to prioritize 'Not Documented' over speculating.
	- Contextual Continuity: Overlap-aware training prevents information loss across page boundaries.
	- Domain Versatility: Seamlessly switches between technical and non-technical document styles.
	\n## 💻 Professional Usage (Grounded Inference)
	To achieve the trained precision level, utilize the following code implementation:
	\n```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_id = 'solvrays/solvrays-llm'
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto', torch_dtype=torch.bfloat16)

	# Universal Grounding Template
	instruction = 'Analyze your internal knowledge base and provide a precise, factual response based strictly on the documentation you have been trained on. If the information is not documented, 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:')

	inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
	with torch.no_grad():
	outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False, repetition_penalty=1.5)

	print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('### Verified Response:')[-1].strip())
	```
	\n## 📊 Technical Specifications
	\| Parameter \| Configuration \|
	\| :--- \| :--- \|
	\| Base Model \| google/gemma-2b-it \|
	\| Fine-tuning Method \| QLoRA (4-bit quantization) \|
	\| LoRA Rank (r) \| 16 \|
	\| LoRA Alpha \| 32 \|
	\| Training Epochs \| 5 \|
	\| Context Strategy \| 512 tokens with 128-token overlap \|
	\n## ⚠️ Risks & Limitations
	- Context Window: Strictly limited to the fine-tuned block size (512 tokens). For longer multi-page queries, RAG (Retrieval Augmented Generation) is recommended.
	- Bias: The model reflects the biases of the provided training documentation.
	- Accuracy: Always verify critical technical numbers against the original source.
	\n---
	Architected and Fine-tuned by Bibek Lama Singtan