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

Libraries

How to use solvrays/solvrays-finetuned-pdf with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solvrays/solvrays-finetuned-pdf")
model = AutoModelForCausalLM.from_pretrained("solvrays/solvrays-finetuned-pdf")
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

vLLM

How to use solvrays/solvrays-finetuned-pdf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solvrays/solvrays-finetuned-pdf"
# 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-finetuned-pdf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solvrays/solvrays-finetuned-pdf

SGLang

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

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

solvrays-finetuned-pdf / README.md

singtan

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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 Finetuned Pdf - Document AI

	## 🌟 Model Overview
	This model is a high-precision fine-tuning of google/gemma-2b-it, specifically architected for Zero-Hallucination Technical Retrieval. It has been trained on a proprietary dataset of technical and architectural documentation to ensure deep contextual grounding.

	### 🚀 Key Capabilities
	- Technical Grounding: Prioritizes factual documentation over generative speculation.
	- Chunk-Aware Memory: Optimized for overlapping document segments (256-token window).
	- Deterministic Precision: Best used with `do_sample=False` for architectural accuracy.

	## 💻 Professional Implementation
	The model requires specific prompt construction to trigger its 'Knowledge Retrieval' mode:

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_id = 'solvrays/solvrays-finetuned-pdf'
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map='auto',
	torch_dtype=torch.bfloat16,
	quantization_config={'load_in_4bit': True}
	)

	def query_model(user_query):
	# High-Precision Retrieval Template
	prompt = f'### Knowledge Retrieval Content: {user_query}\n### Verified Response: '
	inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
	outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
	return tokenizer.decode(outputs[0], skip_special_tokens=True).split('### Verified Response:')[-1].strip()
	```

	## 📊 Technical Specifications
	\| Feature \| Configuration \|
	\| :--- \| :--- \|
	\| Base Model \| google/gemma-2b-it \|
	\| Precision \| BrainFloat16 (BF16) \|
	\| Fine-tuning \| QLoRA (4-bit Normalized Float) \|
	\| LoRA Rank (r) \| 16 \|
	\| LoRA Alpha \| 32 \|
	\| Target Modules \| q, k, v, o, gate, up, down \|
	\| Training Epochs \| 25 \|

	## 🛠 Training Environment
	- Hardware: NVIDIA L4 x 2 (Dual GPU Architecture)
	- Optimizer: Paged AdamW 8-bit
	- Context Length: 256 tokens per block

	## ⚠️ Constraints & Risk Mitigation
	- Out-of-Scope: This model is not intended for general conversation or creative writing. It is a specialized document analyst.
	- Hallucination Control: If information is not present in the internal weights, the model is trained to state 'Not Documented' or provide an empty response for verification.
	- Numerical Accuracy: Always cross-verify critical measurements with original PDF source material.

	---
	Senior AI Architect & Developer: Solvrays