Text Generation
Transformers
Safetensors
llama
falcon3
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use tiiuae/Falcon3-10B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiiuae/Falcon3-10B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tiiuae/Falcon3-10B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-10B-Instruct") model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-10B-Instruct") 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 tiiuae/Falcon3-10B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tiiuae/Falcon3-10B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tiiuae/Falcon3-10B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tiiuae/Falcon3-10B-Instruct
- SGLang
How to use tiiuae/Falcon3-10B-Instruct 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 "tiiuae/Falcon3-10B-Instruct" \ --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": "tiiuae/Falcon3-10B-Instruct", "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 "tiiuae/Falcon3-10B-Instruct" \ --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": "tiiuae/Falcon3-10B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tiiuae/Falcon3-10B-Instruct with Docker Model Runner:
docker model run hf.co/tiiuae/Falcon3-10B-Instruct
Adding Evaluation Results
#2
by Weyaxi - opened
README.md
CHANGED
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---
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library_name: transformers
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tags:
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- falcon3
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base_model: tiiuae/Falcon3-10B-Base
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-
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-
license_name: falcon-llm-license
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license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
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---
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<div align="center">
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@@ -262,3 +357,17 @@ If Falcon3 family were helpful in your work, feel free to give us a cite.
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}
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```
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---
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+
license: other
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library_name: transformers
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tags:
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- falcon3
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base_model: tiiuae/Falcon3-10B-Base
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license_name: falcon-llm-license
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license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
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+
model-index:
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+
- name: Falcon3-10B-Instruct
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+
results:
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+
- task:
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type: text-generation
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name: Text Generation
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+
dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 78.17
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| 23 |
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name: strict accuracy
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| 24 |
+
source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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+
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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+
metrics:
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- type: acc_norm
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value: 44.82
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name: normalized accuracy
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| 39 |
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 25.91
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name: exact match
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| 54 |
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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+
- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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+
metrics:
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- type: acc_norm
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value: 10.51
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| 68 |
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name: acc_norm
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| 69 |
+
source:
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| 70 |
+
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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| 71 |
+
name: Open LLM Leaderboard
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| 72 |
+
- task:
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| 73 |
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type: text-generation
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| 74 |
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name: Text Generation
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| 75 |
+
dataset:
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| 76 |
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 13.61
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| 83 |
+
name: acc_norm
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| 84 |
+
source:
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| 85 |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 38.1
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name: accuracy
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| 101 |
+
source:
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| 102 |
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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---
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<div align="center">
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}
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```
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+
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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+
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/tiiuae__Falcon3-10B-Instruct-details)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |35.19|
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|IFEval (0-Shot) |78.17|
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| 368 |
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|BBH (3-Shot) |44.82|
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| 369 |
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|MATH Lvl 5 (4-Shot)|25.91|
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| 370 |
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|GPQA (0-shot) |10.51|
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| 371 |
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|MuSR (0-shot) |13.61|
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| 372 |
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|MMLU-PRO (5-shot) |38.10|
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| 373 |
+
|