Instructions to use JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype") model = AutoModelForCausalLM.from_pretrained("JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype", 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 JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype
- SGLang
How to use JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype 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 "JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype" \ --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": "JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype", "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 "JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype" \ --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": "JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype with Docker Model Runner:
docker model run hf.co/JuliaKreutzerCohere/tiny-aya-global-prompt-tasktype
Upload folder using huggingface_hub
Browse files- prompts/default.txt +17 -0
- prompts/fill_blanks.txt +17 -0
- prompts/match_letters.txt +17 -0
- prompts/num_to_text.txt +17 -0
- prompts/text_to_num.txt +17 -0
- prompts/translation.txt +17 -0
prompts/default.txt
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
CONTEXT:
|
| 2 |
+
{context}
|
| 3 |
+
|
| 4 |
+
TASK TYPE: `{task_type}`
|
| 5 |
+
|
| 6 |
+
QUERY:
|
| 7 |
+
{query}
|
| 8 |
+
|
| 9 |
+
Read the QUERY instructions carefully — this task type may be unfamiliar. Learn only from CONTEXT, then produce answers in exactly the form the QUERY asks for.
|
| 10 |
+
|
| 11 |
+
How to solve:
|
| 12 |
+
1. Deduce the linguistic rules from the CONTEXT examples only.
|
| 13 |
+
2. Apply those rules to every item in QUERY, in order.
|
| 14 |
+
3. Match the answer unit and format shown by the instructions and CONTEXT (words, letters, digits, transcriptions, etc.).
|
| 15 |
+
4. Put only the bare required answer on each answer line — no glosses, numbering, or commentary.
|
| 16 |
+
|
| 17 |
+
Draft, then verify completeness and format. Finally write a line that says exactly `FINAL ANSWERS:` and, below it, one bare answer per QUERY item in QUERY order — no numbering, no quotes, no extra text.
|
prompts/fill_blanks.txt
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
CONTEXT:
|
| 2 |
+
{context}
|
| 3 |
+
|
| 4 |
+
TASK TYPE: `fill_blanks`
|
| 5 |
+
|
| 6 |
+
QUERY:
|
| 7 |
+
{query}
|
| 8 |
+
|
| 9 |
+
This is a fill-in-the-blanks task. Missing pieces may be whole words, morphemes, or phonetic segments — match the kind of unit CONTEXT uses in the corresponding positions.
|
| 10 |
+
|
| 11 |
+
How to solve:
|
| 12 |
+
1. Study complete CONTEXT examples to recover the paradigm / pattern.
|
| 13 |
+
2. Identify exactly what is missing in each QUERY blank (not what could be invented).
|
| 14 |
+
3. Fill each blank with only the missing form, in the same style as CONTEXT (including transcription brackets if CONTEXT uses them).
|
| 15 |
+
4. Return one filled form per blank, in the order blanks / items appear in QUERY.
|
| 16 |
+
|
| 17 |
+
Draft, then verify every blank has an answer. Finally write a line that says exactly `FINAL ANSWERS:` and, below it, one bare filled form per blank/item — no numbering, no quotes, no extra text.
|
prompts/match_letters.txt
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
CONTEXT:
|
| 2 |
+
{context}
|
| 3 |
+
|
| 4 |
+
TASK TYPE: `match_letters`
|
| 5 |
+
|
| 6 |
+
QUERY:
|
| 7 |
+
{query}
|
| 8 |
+
|
| 9 |
+
This is a matching / multiple-choice letter task. Each QUERY item asks you to choose among labeled options (A, B, C, ...).
|
| 10 |
+
|
| 11 |
+
How to solve:
|
| 12 |
+
1. Extract the mapping or rule set from CONTEXT.
|
| 13 |
+
2. For each numbered QUERY item, evaluate the options against that rule set.
|
| 14 |
+
3. Return only the chosen option letter (A, B, C, ...), uppercase.
|
| 15 |
+
4. Do not repeat the option text, explanations, or punctuation around the letter on the answer lines.
|
| 16 |
+
|
| 17 |
+
Draft, then check you have one letter per QUERY item. Finally write a line that says exactly `FINAL ANSWERS:` and, below it, one bare letter per item in QUERY order — no numbering, no quotes, no extra text.
|
prompts/num_to_text.txt
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
CONTEXT:
|
| 2 |
+
{context}
|
| 3 |
+
|
| 4 |
+
TASK TYPE: `num_to_text`
|
| 5 |
+
|
| 6 |
+
QUERY:
|
| 7 |
+
{query}
|
| 8 |
+
|
| 9 |
+
This is a number-to-text task. CONTEXT shows how digit values are written as number words (or expressions) in the target language; convert each QUERY number into that written form.
|
| 10 |
+
|
| 11 |
+
How to solve:
|
| 12 |
+
1. Infer the number-word construction rules from CONTEXT (bases, multipliers, conjunctions, morphology).
|
| 13 |
+
2. Apply them to each QUERY number.
|
| 14 |
+
3. Return only the number written out in words / forms as in CONTEXT's language style.
|
| 15 |
+
4. Do not output digits on the answer lines and do not add English glosses or commentary.
|
| 16 |
+
|
| 17 |
+
Draft, then verify each QUERY item has a written numeral form. Finally write a line that says exactly `FINAL ANSWERS:` and, below it, one bare written form per item in QUERY order — no numbering, no quotes, no extra text.
|
prompts/text_to_num.txt
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
CONTEXT:
|
| 2 |
+
{context}
|
| 3 |
+
|
| 4 |
+
TASK TYPE: `text_to_num`
|
| 5 |
+
|
| 6 |
+
QUERY:
|
| 7 |
+
{query}
|
| 8 |
+
|
| 9 |
+
This is a text-to-number task. CONTEXT shows how number words (or number expressions) map to values; convert each QUERY expression into digits.
|
| 10 |
+
|
| 11 |
+
How to solve:
|
| 12 |
+
1. Infer the number system from CONTEXT (bases, place values, multipliers, word order).
|
| 13 |
+
2. Apply it to each QUERY expression.
|
| 14 |
+
3. Return only the numeric value in ordinary digits (e.g. 42), with no units or words.
|
| 15 |
+
4. Do not spell numbers out and do not add commentary on the answer lines.
|
| 16 |
+
|
| 17 |
+
Draft, then verify each QUERY item has a digit answer. Finally write a line that says exactly `FINAL ANSWERS:` and, below it, one bare number per item in QUERY order — no numbering, no quotes, no extra text.
|
prompts/translation.txt
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
CONTEXT:
|
| 2 |
+
{context}
|
| 3 |
+
|
| 4 |
+
TASK TYPE: `translation`
|
| 5 |
+
|
| 6 |
+
QUERY:
|
| 7 |
+
{query}
|
| 8 |
+
|
| 9 |
+
This is a translation task. Use only the CONTEXT examples to learn how forms map between languages (or between orthography and meaning), then translate each QUERY item.
|
| 10 |
+
|
| 11 |
+
How to solve:
|
| 12 |
+
1. Align CONTEXT pairs and find systematic correspondences (roots, affixes, word order, agreement).
|
| 13 |
+
2. Apply those rules to each QUERY item in order.
|
| 14 |
+
3. Output only the translated form itself — in the language the QUERY asks for.
|
| 15 |
+
4. Do not add glosses, English explanations, punctuation wrappers, or commentary on the answer lines.
|
| 16 |
+
|
| 17 |
+
Draft, then check completeness and format. Finally write a line that says exactly `FINAL ANSWERS:` and, below it, one bare translation per QUERY item, in QUERY order — no numbering, no quotes, no extra text.
|