Instructions to use Bogula/pinktilde32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bogula/pinktilde32 with Transformers:

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

pipe = pipeline("text-generation", model="Bogula/pinktilde32")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Bogula/pinktilde32")
model = AutoModelForMultimodalLM.from_pretrained("Bogula/pinktilde32")
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 Bogula/pinktilde32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bogula/pinktilde32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bogula/pinktilde32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bogula/pinktilde32

SGLang

How to use Bogula/pinktilde32 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 "Bogula/pinktilde32" \
    --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": "Bogula/pinktilde32",
		"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 "Bogula/pinktilde32" \
        --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": "Bogula/pinktilde32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bogula/pinktilde32 with Docker Model Runner:
```
docker model run hf.co/Bogula/pinktilde32
```

pinktilde32 / README.md

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metadata

license: cc-by-4.0
base_model: TildeAI/TildeOpen-30b-64k
base_model_relation: finetune
language:
  - de
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - rag
  - retrieval-augmented-generation
  - summarization
  - information-extraction
  - instruction-following
  - german
  - english
  - chatml
datasets:
  - nvidia/Nemotron-Instruction-Following-Chat-v1
  - DiscoResearch/germanrag
  - abisee/cnn_dailymail
  - wikimedia/wikipedia

pinktilde32

A chat / instruct model specialized for retrieval-augmented generation (RAG), summarization, information extraction, and structured Markdown output, fine-tuned from TildeAI/TildeOpen-30b-64k — a 30B European multilingual base model with a 64k context window (extended via YaRN). Focus languages: German + English.

Intended use

Answering questions strictly from a provided context (RAG), with source citations [n].
Honest refusal when the answer is not in the context (no hallucination).
Summarization and information extraction from long inputs.
Structured output in Markdown (headings, bullet lists, tables).

Not intended for: code generation, free-standing factual answers without context, clinical/legal advice.

Prompt format

The model uses chatml (<|im_start|> / <|im_end|>). Recommended system prompt (the RAG contract):

Answer the question or extract the information STRICTLY from the provided context.
Cite the sources you use as [n]. Present the answer in clear Markdown structure.
If the information is not in the context, say so honestly and do not guess.

Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Bogula/pinktilde32"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

system = ("Answer strictly from the context. Cite sources as [n]. Use Markdown. "
          "If the info is missing, say so honestly.")
context = "[1] Muster AG reported revenue of EUR 142M in 2025.\n[2] ..."
messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": f"Context:\n{context}\n\nQuestion: What was the 2025 revenue?"},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 return_tensors="pt", return_dict=True).to(model.device)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.3,
                     eos_token_id=tok.convert_tokens_to_ids("<|im_end|>"))
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Training

Method: LoRA SFT (all linear layers + embed_tokens/lm_head), then merged into the base model.
Training context length: 32k (sequence_len=32768, sample packing).
Format: chatml; loss computed on assistant turns only.

Data mix

Source	Language	Purpose
nvidia/Nemotron-Instruction-Following-Chat-v1	EN	Instruction / format adherence, structured outputs
DiscoResearch/germanrag	DE	RAG grounding with citations + "unanswerable" cases
abisee/cnn_dailymail	EN	Summarization (Markdown)
wikimedia/wikipedia (de, business/psychology)	DE	Summarization (Markdown)
Internal company dialogues	DE	Domain / style anchor

Limitations

Long context: The target behaviors (grounding, formatting) were trained up to ~32k. For inputs between 32k and 64k only the base long-context capability of TildeOpen applies, where reliability may degrade.
Language balance: The instruction-following data is English; German format adherence benefits from transfer but may lag behind English.
May still occasionally hallucinate or imperfectly follow formatting instructions. Verify outputs.

License & attribution

The base model TildeOpen-30b-64k is licensed under CC-BY-4.0; this derivative is released under the same license. Training data includes, among others: Nemotron-Instruction-Following-Chat-v1 (ODC-BY / CC-BY-4.0), DiscoResearch/germanrag (CC-BY-SA-4.0, derived from GermanDPR), CNN/DailyMail, and German Wikipedia (CC-BY-SA).

Note: Some training sources are under share-alike licenses (CC-BY-SA). Whether and to what extent these propagate to model weights is not legally settled. This is not legal advice — please verify license compliance for your specific use case and attribute the sources accordingly.